larnaboldi/XTR-Zero-unsupervised-noprompts-v2

PyLate

This is a PyLate model trained on the reddit_title_body, amazon_reviews, paq, s2orc_citation_titles, s2orc_title_abstract, s2orc_abstract_citation, s2orc_abstract_body, wikianswers, wikipedia, gooaq, codesearch, yahoo_title_answer, agnews, amazonqa, yahoo_qa, yahoo_title_question, ccnews, npr, eli5, cnn, stackexchange_duplicate_questions, stackexchange_title_body, stackexchange_body_body, sentence_compression, wikihow, altlex, quora, simplewiki and squad datasets. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Model Details

Model Description

• Model Type: PyLate model
• Document Length: 180 tokens
• Query Length: 32 tokens
• Output Dimensionality: 128 tokens
• Similarity Function: MaxSim
• Training Datasets:
  • reddit_title_body
  • amazon_reviews
  • paq
  • s2orc_citation_titles
  • s2orc_title_abstract
  • s2orc_abstract_citation
  • s2orc_abstract_body
  • wikianswers
  • wikipedia
  • gooaq
  • codesearch
  • yahoo_title_answer
  • agnews
  • amazonqa
  • yahoo_qa
  • yahoo_title_question
  • ccnews
  • npr
  • eli5
  • cnn
  • stackexchange_duplicate_questions
  • stackexchange_title_body
  • stackexchange_body_body
  • sentence_compression
  • wikihow
  • altlex
  • quora
  • simplewiki
  • squad

Model Sources

• Documentation: PyLate Documentation
• Repository: PyLate on GitHub
• Hugging Face: PyLate models on Hugging Face

Full Model Architecture

ColBERT(
  (0): Transformer({'max_seq_length': 179, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)

Usage

First install the PyLate library:

pip install -U pylate

Retrieval

Use this model with PyLate to index and retrieve documents. The index uses FastPLAID for efficient similarity search.

Indexing documents

Load the ColBERT model and initialize the PLAID index, then encode and index your documents:

from pylate import indexes, models, retrieve

# Step 1: Load the ColBERT model
model = models.ColBERT(
    model_name_or_path="pylate_model_id",
)

# Step 2: Initialize the PLAID index
index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
    override=True,  # This overwrites the existing index if any
)

# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]

documents_embeddings = model.encode(
    documents,
    batch_size=32,
    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
    show_progress_bar=True,
)

# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
    documents_ids=documents_ids,
    documents_embeddings=documents_embeddings,
)

Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:

# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
)

Retrieving top-k documents for queries

Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:

# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)

# Step 2: Encode the queries
queries_embeddings = model.encode(
    ["query for document 3", "query for document 1"],
    batch_size=32,
    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
    show_progress_bar=True,
)

# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
    queries_embeddings=queries_embeddings,
    k=10,  # Retrieve the top 10 matches for each query
)

Reranking

If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:

from pylate import rank, models

queries = [
    "query A",
    "query B",
]

documents = [
    ["document A", "document B"],
    ["document 1", "document C", "document B"],
]

documents_ids = [
    [1, 2],
    [1, 3, 2],
]

model = models.ColBERT(
    model_name_or_path="pylate_model_id",
)

queries_embeddings = model.encode(
    queries,
    is_query=True,
)

documents_embeddings = model.encode(
    documents,
    is_query=False,
)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)

Evaluation

Metrics

Py Late Information Retrieval

• Dataset: ['NanoClimateFEVER', 'NanoDBPedia', 'NanoFEVER', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNFCorpus', 'NanoNQ', 'NanoQuoraRetrieval', 'NanoSCIDOCS', 'NanoArguAna', 'NanoSciFact', 'NanoTouche2020']
• Evaluated with pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator

Metric	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
MaxSim_accuracy@1	0.24	0.68	0.92	0.48	0.8	0.38	0.46	0.56	0.88	0.5	0.26	0.64	0.6122
MaxSim_accuracy@3	0.44	0.92	1.0	0.64	0.96	0.64	0.54	0.72	0.98	0.7	0.68	0.8	0.8571
MaxSim_accuracy@5	0.62	0.98	1.0	0.72	0.98	0.72	0.58	0.78	1.0	0.78	0.76	0.82	0.9184
MaxSim_accuracy@10	0.68	1.0	1.0	0.74	0.98	0.88	0.74	0.86	1.0	0.9	0.82	0.88	0.9796
MaxSim_precision@1	0.24	0.68	0.92	0.48	0.8	0.38	0.46	0.56	0.88	0.5	0.26	0.64	0.6122
MaxSim_precision@3	0.16	0.6267	0.3533	0.3067	0.4933	0.2133	0.3733	0.2467	0.4067	0.3867	0.2267	0.2733	0.5714
MaxSim_precision@5	0.144	0.552	0.212	0.232	0.32	0.144	0.336	0.164	0.264	0.308	0.152	0.18	0.5551
MaxSim_precision@10	0.086	0.5	0.108	0.142	0.168	0.088	0.296	0.094	0.138	0.21	0.082	0.098	0.4531
MaxSim_recall@1	0.135	0.0637	0.8567	0.2526	0.4	0.38	0.0247	0.53	0.7673	0.1057	0.26	0.63	0.0433
MaxSim_recall@3	0.219	0.2003	0.9633	0.4187	0.74	0.64	0.0587	0.67	0.942	0.2387	0.68	0.775	0.1181
MaxSim_recall@5	0.3123	0.2562	0.9633	0.5188	0.8	0.72	0.0979	0.73	0.986	0.3167	0.76	0.815	0.1911
MaxSim_recall@10	0.3557	0.3831	0.9733	0.585	0.84	0.88	0.1529	0.83	0.9967	0.4307	0.82	0.87	0.2986
MaxSim_ndcg@10	0.2944	0.6143	0.9431	0.5133	0.787	0.6272	0.3477	0.6845	0.9423	0.4168	0.5579	0.7623	0.509
MaxSim_mrr@10	0.3774	0.7988	0.9567	0.5819	0.8783	0.5467	0.5224	0.658	0.934	0.6117	0.4712	0.7255	0.7494
MaxSim_map@100	0.238	0.4847	0.9248	0.4519	0.7212	0.5537	0.1472	0.6338	0.9166	0.3282	0.4826	0.7299	0.3651

Nano BEIR

• Dataset: NanoBEIR_mean
• Evaluated with pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator

Metric	Value
MaxSim_accuracy@1	0.5702
MaxSim_accuracy@3	0.7598
MaxSim_accuracy@5	0.8199
MaxSim_accuracy@10	0.8815
MaxSim_precision@1	0.5702
MaxSim_precision@3	0.3568
MaxSim_precision@5	0.2741
MaxSim_precision@10	0.1895
MaxSim_recall@1	0.3422
MaxSim_recall@3	0.5126
MaxSim_recall@5	0.5744
MaxSim_recall@10	0.6474
MaxSim_ndcg@10	0.6154
MaxSim_mrr@10	0.6779
MaxSim_map@100	0.5367

Training Details

Training Datasets

reddit_title_body

• Dataset: reddit_title_body
• Size: 66,204,599 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 7 tokens mean: 18.04 tokens max: 32 tokens	min: 20 tokens mean: 31.83 tokens max: 32 tokens

• Samples:

  query	document
  Prospective UNCW transfer?	Hey Reddit, I am transferring from Florida State to hopefully UNCW this spring. What can you guys tell me about the school that would be helpful? Some background info: I am transferring due to the fact that the only thing to do at FSU is workout and drink (not much of a drinker). I am majoring in biology and have a 3.7 GPA. Anything that you feel is useful to know about the school is appreciated. Thanks guys.
  Calling for another Meet-up! The force is strong.	The time has come. The pull to meet-up with other Jax Redditors is strong, my son. We must use the force and decide where to meet-up. Jax Jedi's do not succumb to the dark side of average places, go with your exceptional suggestions. Yoda say "Must is beer, I say. Welcome are all other suggestions, mmmmmm."
  I see your Best Customer E-Mail Ever, and raise you my e-mail from an appreciative customer.	A little background, I'm a software support tech for a medium-large software company, and usually provide support on our Live Chat feature. You know the one. After chatting with one pleasant customer, several times per day over several weeks, I nominated him/their company for Customer of the Month. When they recieve their "Thanks for being awesome" customer box, I get this email: Subject: Epic customer appreciation box Body: Guy and the dog with "Oh you!" face. I laughed, and laughed. Unsuspecting tech support is floored by internet humor from relatively normal customer.

• Loss: pylate.losses.cached_contrastive.CachedContrastive

amazon_reviews

• Dataset: amazon_reviews
• Size: 39,357,860 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 6 tokens mean: 14.18 tokens max: 32 tokens	min: 11 tokens mean: 30.56 tokens max: 32 tokens

• Samples:

  query	document
  It works well but the headphone apparatus falls too deep ...	It works well but the headphone apparatus falls too deep for any of my headphones to work :( I wish there was an adaptor included to solve this problem
  Very nice frame! Snaps open at the front like a ...	Very nice frame! Snaps open at the front like a real movie poster frame which I think is cool. It worked perfectly for a document I had that was this size, looks great with the green color it is.
  The shoes look very good. Size wise	The shoes look very good. Size wise, they fit well. The ultimate test will be how they last and time will tell.

• Loss: pylate.losses.cached_contrastive.CachedContrastive

paq

• Dataset: paq
• Size: 53,874,545 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 9 tokens mean: 14.67 tokens max: 23 tokens	min: 32 tokens mean: 32.0 tokens max: 32 tokens

• Samples:

  query	document
  how long does it take to complete the orbit of 70 ophiuchi	70 Ophiuchi sequence dwarf of spectral type K0, while the secondary is an orange dwarf of spectral type K4. The two stars orbit each other at an average distance of 23.2 AUs. But since the orbit is highly elliptical (at e=0.499), the separation between the two varies from 11.4 to 34.8 AUs, with one orbit taking 83.38 years to complete. In 1855, William Stephen Jacob of the Madras Observatory claimed that the orbit of the binary showed an anomaly, and it was "highly probable" that there was a "planetary body in connection with this system". This is the first attempt to use
  who is the author of the switchman	The Switchman The Switchman (Original title: El Guardagujas) is an existentialist short story by Mexican writer Juan José Arreola. The short story was originally published as a "confabulario", a word created in Spanish by Arreola, in 1952, in the collection "Confabulario and Other Inventions". It was republished ten years later along with other published works by Arreola at that time in the collection "El Confabulario total". The story revolves around a "stranger" who wishes to travel to the town of T. by train, but is quickly met by a "switchman" who tells him more and more fantastical stories about the
  what name is given to a narrow vertical aperture in a fortification through which an ar	Arrowslit An arrowslit (often also referred to as an arrow loop, loophole or loop hole, and sometimes a balistarium) is a narrow vertical aperture in a fortification through which an archer can launch arrows. The interior walls behind an arrow loop are often cut away at an oblique angle so that the archer has a wide field of view and field of fire. Arrow slits come in a remarkable variety. A common and recognizable form is the cross, accommodating the use of both the longbow and the crossbow. The narrow vertical aperture permits the archer large degrees of freedom to

• Loss: pylate.losses.cached_contrastive.CachedContrastive

s2orc_citation_titles

• Dataset: s2orc_citation_titles
• Size: 7,722,225 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 8 tokens mean: 22.22 tokens max: 32 tokens	min: 8 tokens mean: 21.89 tokens max: 32 tokens

• Samples:

  query	document
  Purulent pericarditis. Clinical considerations with reference to 26 cases.	Purulent Pericarditis: Report of 2 Cases and Review of the Literature
  High-Resolution Controller Data Performance Measures for Optimizing Divergent Diamond Interchanges and Outcome Assessment for Drone Video	An Advanced Signal Phasing Scheme for Diverging Diamond Interchanges
  Silurian subaqueous slide conglomerate, Addison, Maine	Bimodal Silurian and Lower Devonian volcanic rock assemblages in the Machias-Eastport area, Maine

• Loss: pylate.losses.cached_contrastive.CachedContrastive

s2orc_title_abstract

• Dataset: s2orc_title_abstract
• Size: 36,051,582 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 8 tokens mean: 20.44 tokens max: 32 tokens	min: 20 tokens mean: 31.89 tokens max: 32 tokens

• Samples:

  query	document
  2′–5′-Oligoadenylates (2–5A) As Mediators of Interferon Action. Synthesis and Biological Activity of New 2–5A Analogues	Double-stranded RNA (dsRNA) is a potent inhibitor of protein synthesis in extracts of interferon-treated cells. One of the mechanisms that has been proposed to explain this inhibition of protein synthesis is by the 2–5A pathway (1). Interferon induces the synthesis of an enzyme, 2–5A synthetase, which upon activation by dsRNA generates 2–5A from ATP. This 2–5A activates a pre-existing endonuclease for cleavage of single-stranded RNA. The biological activity of 2–5A is rapidly lost due to cleavage of the 2′–5′ internucleotide bond by a specific 2′–5′ phosphodiesterase starting at the 3′-end. This rapid cleavage and the poor uptake of 2–5A in intact cells, the latter because of its ionic character, limit the potential of 2–5A as a useful approach to the treatment of virus infections or cancer.
  p-adic L-functions and Bernoulli Numbers	In this chapter we shall construct p-adic analogues of Dirichlet L-functions. Since the usual series for these functions do not converge p-adically, we must resort to another procedure. The values of ( L\left( {s,\chi } \right)) at negative integers are algebraic, hence may be regarded as lying in an extension of ( {\mathbb{Q}_p}). We therefore look for a p-adic function which agrees with ( L\left( {s,\chi } \right)) at the negative integers. With a few minor modifications, this is possible.
  Wood Pile Structure of Three-Dimensional Photonic Crystal Band Gap Characteristics	Based on the plane wave expansion method,wood pile structure three-dimensional photonic crystal band gap characteristics was studied.Silicon material for wood structure photonic crystals,the change in the structure of strip width and length,is obtained when the wood pile structure width of 5μm,7μm height is formed when the band gap structure of wide band gap width,in 0.2899—0.3804Hz,0.0905Hz.Change form wood pile structure in three-dimensional photonic crystal materials,get the germanium material wood structure shape three-dimensional photonic band gap structure in 0.2585—0.3500Hz,the band gap width of 0.0915Hz,band gap compared to silicon and silicon carbide material is wide.Conclusion for the preparation of three-dimensional photonic crystals provide reference.

• Loss: pylate.losses.cached_contrastive.CachedContrastive

s2orc_abstract_citation

• Dataset: s2orc_abstract_citation
• Size: 7,639,890 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 28 tokens mean: 32.0 tokens max: 32 tokens	min: 24 tokens mean: 31.99 tokens max: 32 tokens

• Samples:

  query	document
  Abstract Temperature modulated differential scanning calorimetry (TMd.s.c.) was applied in a study of syndiotactic polypropylene. The crystallites melt from their lateral surfaces only and the kinetics shows up in the imaginary part c ″ of the complex specific heat. Theoretical analysis predicts and experiments confirm that c ″ increases linearly with the underlying mean heating rate and the modulation period. Furthermore, it can be shown that c ″ is inversely related to the superheating effective during melting. Use of the relation yields for syndiotactic polypropylene values in agreement with direct measurements employing conventional d.s.c.	Crystal melting behavior of indium and isotactic polypropylene has been examined by differential scanning calorimetry of heat flux type in terms of the heating rate, (\beta ), dependence. The melting shows the dependence characterized by a power, (z), of the shift in peak temperature in proportion to (\beta ^{\text{z}}). The power, (z), differentiates the melting with and without superheating. For polymer crystal melting, intrinsic nature of the broad melting region with a fractional power, (z,\le,1/2), due to superheating of melting kinetics has been reconfirmed experimentally. On the other hand, the crystal melting of indium, which is supposed to proceed with negligible superheating, showed the shift in peak temperature with the power in the range of (1/2,\le,z \le,1), depending on sample mass, which is due to instrumental thermal lag predicted by the Mraw’s model consisting of lumped elements. The (\beta ) dependence is influenced by the thermal lag determined by ...
  This article examines anti-racist strategies employed in Finnish children’s literature. The examples from four stories illustrate that certain physical characteristics and cultural markers can become strong signifiers of nationality, that is Finnishness. The characters in these stories have to cope with experiences of exclusion and loneliness before the people around them learn that difference and diversity do not change the fact that all humans are worth the same. However, the paper argues that the intended positive outcome of books with a strong anti-racist agenda threatens to be lost as heavily accentuated moral lessons often become counterproductive. The paper demonstrates some of the changes that have taken place in Finnish children’s literature during the past two decades and addresses significant cultural and societal issues that affect children’s everyday lives.	Abstract: In this article, representations of multiculturalism in Swedish and Finnish picturebooks are examined through the Forskolan Ravlyan and Tatu and Patu series. In the article, multiculturalism is understood and studied with an intersectional approach. This means considering sociocultural categorizations such as ethnicity, gender, nationality and disability to be meaningful to the existing social, political and economic structures of societies. These categorizations are seen to have the power to reproduce and circulate dominant discourses that effect the social inclusion and exclusion of certain groups of people. Thus, the social categories are examined as performative textual discourses, meaning that texts are acknowledged to be not only reflecting, but also creating social reality. Both series present diversity as an integrated part of the story by means of non-explicit multiculturalism. The analysis reveals that both series of books contain representations of diversity that c...
  Rosai–Dorfman disease (RDD) is usually characterized by painless bilateral cervical lymphadenopathy associated with fever and leukocytosis. Although the disease may occur outside lymphnodes, manifestation of skeletal system occurs in less than 8% of cases. In addition, presentation of this disease in a purely skeletal form without lymph nodes involvement is extremely uncommon. This case report describes a 17-year-old female with a pure skeletal presentation of RDD in the fibula. Trocar biopsy was performed, and immunohistochemical staining using S100 and CD68 was done to confirm the diagnosis.	We report a case of extranodal Rosai-Dorfman disease (RDD) (sinus histiocytosis with massive lymphadenopathy) presenting with a solitary active lesion of the femur.

• Loss: pylate.losses.cached_contrastive.CachedContrastive

s2orc_abstract_body

• Dataset: s2orc_abstract_body
• Size: 6,550,431 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 9 tokens mean: 31.97 tokens max: 32 tokens	min: 32 tokens mean: 32.0 tokens max: 32 tokens

• Samples:

  query	document
  One of the goals of the 5G Communication Automotive Research and innovation (5GCAR) project has been to evaluate and propose system architecture enhancements aiming at supporting the strict requirements of vehicle-to-everything (V2X) use cases. In this paper, we provide an overview of 3GPP 5G system architecture, which is used as a baseline architecture in the project, and we present the main architectural enhancements introduced by 5GCAR. The work of the project focused on the following categories: (i) end-to-end security, also including aspects of privacy; (ii) network orchestration and management; (iii) network procedures; (iv) edge computing enhancements; and (v) multi-connectivity cooperation. The enhancements introduced by 5GCAR to above-listed categories are discussed in this paper, while a more detailed analysis of some selected features is presented. Figure 2. Reference point representation of the 5G system architecture [6].The network functions repository function (NRF) is us...	Introduction The automotive sector is considered to be one of the most prominent verticals that will benefit from the capabilities of the upcoming 5G cellular networks [1,2]. Vehicular applications cover a wide range of use cases and thus a large set of associated requirements. Examples include very high data rates and timely service delivery, while also considering ultra-low communication latencies, just to mention a few. Complex scenarios where vehicles communicate among themselves and also with nearby road infrastructure, road users, clouds, etc.-also known as vehicle-to-everything (V2X) communications-will not only leverage 5G network but will play a key role in its design. The H2020 5G PPP Phase 2 project 5G Communication Automotive Research and innovation (5GCAR) [3] worked towards the definition of enhancements in terms of system architecture, security, and privacy, specifically targeting automotive applications. In particular, 5GCAR considered five different classes of use c...
  The Queer History Walking Tour is an annually recurring event during Dublin's official Pride festivities. Created and led by the 'Godfather of Gay,' Tonie Walsh, the walks seek to extend stories from the Irish Queer Archive (IQA) into the everyday fabric of the city, contributing to a processual queering of Irish heteronormative histories. As an activist form of public pedagogy, the walking tour encourages a relational understanding of queer cultural heritage through mobile, embodied, and emotional interactions. This paper argues that the walking tour works as an anarchive that contributes to a growing, intersectional understanding of LGBTQ+ experiences and queer futures, facilitated by peripatetic practices. In response to pervasive cis-male homonormativity at Pride, Dr Mary McAuliffe, a queer feminist woman, is the latest tour guide who includes historical stories of lesbian women, trans people, and gay men. Through engaging with this diversity of historical experiences, guides signa...	Introduction Dublin Pride does not consist of one parade, but two. Every year, Dublin Pride includes a 'mini parade:' a free Queer History Walking Tour created and led by Tonie Walsh. As a founder of the Irish Queer Archive (IQA), co-founder of the Gay Community News (GCN) and long-time gay rights activist, Walsh is well known within the LGBTQ+ community in Ireland as the 'Godfather of Gay' (Mullally, 2018). The tour is highly popular and can draw up to 150 attendees, and sometimes includes collaborations with other historians with their own stories to tell. i The tour includes pausing alongside places of key significance in queer Irish history, be it a historical place that no longer materially exists, or one that has remained unchanged. This paper will draw on Walsh's walking tour to illustrate how walking tours generate a relational understanding of queer cultural heritage through mobile, embodied and emotional interactions with places and other queer people. I argue that, despit...
  A definitive diagnosis of salivary gland tumors is extremely difficult to make without evaluating the entire tumor and conducting immunohistochemical examinations. In this study, we aimed to examine and compare the expression patterns of the tumor protein TP D52 family, including TPD52, TPD53, and TPD54, in salivary gland tumor cells by using immunohistochemical staining. Among over 30 benign and malignant salivary gland tumors with extensive and diverse morphological features and overlapping histological similarities, we selected Warthin s tumor and pleomorphic adenoma to represent benign salivary gland tumors and mucoepidermoid carcinoma to represent malignant ones. Tumor samples were fixed in 10 buffered formalin and embedded in paraffin. Then, immunohistochemical staining was performed using antibodies against TPD52, TPD53, and TPD54. Neither the benign salivary gland tumors nor mucoepidermoid carcinoma stained for TPD52. However, the intensity of TPD53 and TPD54 staining was found...	Introduction The salivary glands are exocrine organs that produce saliva and are complex tissues composed of ductal, acinar, myoepithelial, and basal cells 1 . Collectively called as luminal cells, ductal and acinar cells are present on the luminal side of the salivary duct system. Myoepithelial and basal cells are located on the basement membrane around the luminal cells and are thus called abluminal cells 2 . In general, 3 types of acini namely serous, mucinous, and mixed and ducts i.e., intercalated, striated, and excretory are found in the salivary glands. The acini and intercalated ducts are surrounded by myoepithelial cells, whereas the striated and excretory ducts are surrounded by basal cells 3 . Tumors of the salivary glands comprise less than 1 of all neoplasms in the body 4 ; however, there are more than 30 benign and malignant salivary gland tumors with extensive and diverse morphologies yet overlapping histological similarities 4 . Hence, it is extremely difficult to d...

• Loss: pylate.losses.cached_contrastive.CachedContrastive

wikianswers

• Dataset: wikianswers
• Size: 10,087,503 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 7 tokens mean: 14.22 tokens max: 32 tokens	min: 7 tokens mean: 14.01 tokens max: 32 tokens

• Samples:

  query	document
  What is the average weight for a 4'11 14 year old girl?	What is the average weight for a 4' 9 14 year old girl?
  The Fahrenheit temperature reading is 98 degrees on a hot summer day Wh is this reading on the Kelvin scale?	Fahrenheit temp 98 on hot summer day what is this reading on the kelvin scale?
  What is the word for you in Japanese?	What word in japanese i loveyou?

• Loss: pylate.losses.cached_contrastive.CachedContrastive

wikipedia

• Dataset: wikipedia
• Size: 6,198,049 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 4 tokens mean: 8.86 tokens max: 28 tokens	min: 23 tokens mean: 31.97 tokens max: 32 tokens

• Samples:

  query	document
  Pristimantis lichenoides	Pristimantis lichenoides (rana camuflada in Spanish) is a species of frogs in the family Craugastoridae. It is endemic to Colombia and is only known from the vicinity of its type locality near Samaná in the Caldas Department, on the eastern slope of the Cordillera Central (Colombian Andes). The specific name lichenoides refers to its lichen-like dorsal coloration as well as its habit of being plastered to rock surfaces, resembling lichens growing on rocks. Description Adult males measure and adult females in snout–vent length. The head is as wide as the body and wider than it is long. The snout is rounded in dorsal view but subtruncate in lateral view. The tympanum is small but visible, with its upper edge hidden by the thick supratympanic fold. The fingers have lateral keels and round terminal discs. The lateral keels of the toes coalesce as basal webbing; the toe discs are slightly smaller than those on the fingers. Dorsal skin bears granules. Dorsal coloration is dark green to pa...
  Askim station	Askim Station () is located at Askim, Norway on the Eastern Østfold Line. The railway station is served by the Oslo Commuter Rail line L22 from Oslo Central Station. The station was opened with the eastern line of Østfold Line in 1882. Railway stations in Askim Railway stations on the Østfold Line Railway stations opened in 1882 1882 establishments in Norway
  Mildred Alango	Mildred Akinyi "Milka" Alango (born 10 March 1989 in Mombasa) is a Kenyan taekwondo practitioner. Alango qualified for the women's 49 kg class at the 2008 Summer Olympics in Beijing, after winning the championship title from the African Qualification Tournament in Tripoli, Libya. She lost the preliminary match to China's Wu Jingyu, who was able to score seven points at the end of the game. Because her opponent advanced further into the final match, Alango took advantage of the repechage round by defeating Sweden's Hanna Zajc on the superiority rule, after the pair had tied 2–2. She progressed to the bronze medal match, but narrowly lost the medal to Venezuela's Dalia Contreras, with a sudden death score of 0–1. References External links NBC 2008 Olympics profile 1989 births Living people Kenyan female taekwondo practitioners Olympic taekwondo practitioners of Kenya Taekwondo practitioners at the 2008 Summer Olympics Sportspeople from Mombasa

• Loss: pylate.losses.cached_contrastive.CachedContrastive

gooaq

• Dataset: gooaq
• Size: 1,281,138 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 9 tokens mean: 12.49 tokens max: 22 tokens	min: 14 tokens mean: 31.58 tokens max: 32 tokens

• Samples:

  query	document
  what is psma pet ct scan?	A PSMA study, also called a ProstaScint® scan, is an imaging test to locate and determine the extent of prostate cancer. ... The study involves a special molecule called a monoclonal antibody developed in a laboratory and designed to bind to the prostate-specific membrane antigen on cancer cells.
  how many calories do you burn walking up mount snowdon?	You will burn through around 2,000 calories climbing Snowdon.
  ankara is the capital city of?	Ankara, formerly known as Angora, city, capital of Turkey, situated in the northwestern part of the country.

• Loss: pylate.losses.cached_contrastive.CachedContrastive

codesearch

• Dataset: codesearch
• Size: 864,023 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 6 tokens mean: 25.48 tokens max: 32 tokens	min: 28 tokens mean: 32.0 tokens max: 32 tokens

• Samples:

  query	document
  Similar to {@link #getOrCreateLocalTransaction(Transaction, boolean)} but with a custom global transaction factory.	public LocalTransaction getOrCreateLocalTransaction(Transaction transaction, boolean implicitTransaction, Supplier gtxFactory) { LocalTransaction current = localTransactions.get(transaction); if (current == null) { if (!running) { // Assume that we wouldn't get this far if the cache was already stopped throw log.cacheIsStopping(cacheName); } GlobalTransaction tx = gtxFactory.get(); current = txFactory.newLocalTransaction(transaction, tx, implicitTransaction, currentTopologyId); if (trace) log.tracef("Created a new local transaction: %s", current); localTransactions.put(transaction, current); globalToLocalTransactions.put(current.getGlobalTransaction(), current); if (notifier.hasListener(TransactionRegistered.class)) { // TODO: this should be allowed to be async at some point CompletionStages.join(notifier.notifyTransactionRegistered(tx, ...
  // formatArgs converts the given args to pretty-printed, colorized strings.	func formatArgs(args ...interface{}) []string { formatted := make([]string, 0, len(args)) for _, a := range args { s := colorize(pretty.Sprint(a), cyan) formatted = append(formatted, s) } return formatted }
  log request in history @access private @param $message string @return void @since 3.0 @package Gcs\Framework\Core\Engine	private function _setHistory($message) { $this->addError('URL : http://' . $this->request->env('HTTP_HOST') . $this->request->env('REQUEST_URI') . ' (' . $this->response->status() . ') / SRC "' . $this->request->src . '" / CONTROLLER "' . $this->request->controller . '" / ACTION "' . $this->request->action . '" / CACHE "' . $this->request->cache . '" / ORIGIN : ' . $this->request->env('HTTP_REFERER') . ' / IP : ' . $this->request->env('REMOTE_ADDR') . ' / ' . $message, 0, 0, 0, LOG_HISTORY); }

• Loss: pylate.losses.cached_contrastive.CachedContrastive

yahoo_title_answer

• Dataset: yahoo_title_answer
• Size: 276,726 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 7 tokens mean: 18.34 tokens max: 32 tokens	min: 6 tokens mean: 30.09 tokens max: 32 tokens

• Samples:

  query	document
  Who to contact in the philippines to install Supersports SA cable Channel?	go to this web site www.dishtv.sa.com\n or ask ur cable operator\n\nor contact this number 0091234537835\n\n\n\ni hope this helps
  What does "you're preaching to the choir" mean?	"preaching to the choir" means trying to make a point to someone who already agrees with your position. The analogy meaning that those in the choir are already familiar with the preaching... it's the others that likely need it.
  Does anyone know a good site where i can find a detailed but simply explained explanation on why henry VIII?	Henry VIII and the break with Rome\nClick on the * words in the site to show:\n\nPower - "Henry had hoped to resolve the issue of who was to succeed him"\n\nMoney - "As well as his desire for the divorce, there was a strong financial incentive for Henry to deny the authority of the Pope"\n\nFaith - "Although Henry's reformation broke with the papacy, his own religious beliefs were orthodox"\n\nLove - "Henry was in love with Anne Boleyn"

• Loss: pylate.losses.cached_contrastive.CachedContrastive

agnews

• Dataset: agnews
• Size: 420,288 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 8 tokens mean: 14.59 tokens max: 32 tokens	min: 12 tokens mean: 30.68 tokens max: 32 tokens

• Samples:

  query	document
  Italy coming out of Washington's shadow	Long considered something of a junior partner among Europe's elite nations, Italy is carving out a hefty role in world affairs. Rome is contributing the largest contingent to the U.N. peacekeeping force in Lebanon, has claimed a role in negotiations with Iran and is rallying European governments around the idea that Italy can form a counterweight to American might.
  Iran, Europe Fail to Agree on Uranium Enrichment, IRNA Reports	Iran and Europe failed to reach an accord on Tehran's uranium enrichment program, the state-owned Iranian news agency said, increasing the chances the US may call for United Nations sanctions against the Islamic nation.
  Omicidio Desirée, la Cassazione "La pena per Erra va inasprita"	La sentenza farà da apripista per la futura giurisprudenzaCon il nuovo processo a Milano, l'imputato rischia l'ergastolo Omicidio Desirée, la Cassazione "La pena per Erra va inasprita" Il nuovo processo si celebrerà all'Assise d'appello di Milano

• Loss: pylate.losses.cached_contrastive.CachedContrastive

amazonqa

• Dataset: amazonqa
• Size: 226,137 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 7 tokens mean: 22.22 tokens max: 32 tokens	min: 18 tokens mean: 30.54 tokens max: 32 tokens

• Samples:

  query	document
  Wondering how people get the wrinkles out from the packaging? Iron or wash and hang damp,maybe?	I sprayed with water (misted it) then ironed it. Most wrinkles came out and what did not, eventually came out from the steam of the shower. Good- luck
  Why is it that most of the Janome users previously owned Singer or Kenmore? Anything has to be better than either of those--so what's the real benefit of a Janome HD3000 vs a Pfaff?	I can't tell you anything about Pfaff because I have never owned a Pfaff. When I bought the Janome HD3000 I was looking for a heavy duty sewing machine that would sew through layered heavy fabrics, such as denim, etc. I had a Singer at the time, and had always owned Singers, and had noticed that with each new Singer I bought, the quality was less than the previous Singer. I don't know what happened to Singer, but in my opinion they have put out a less and less quality product over the past 10 to 15 years. The question I asked in my search engine was something like "what is a good heavy duty sewing machine". That led me to a demonstration video where I watched someone using the Janome to sew through the depth of fabric layers that I needed. And when I bought the machine, it worked just like in the video. It sails through layers of fabric that used to invariably tangle up and stop the Singer.
  I would like to use this for storing thread. I need the drawers to be a least 4" high . Also, do the tops come off the container.	The drawers are only 2 inches high. They slide out . Each has a picture of a big Lego head on top the top does snap off.

• Loss: pylate.losses.cached_contrastive.CachedContrastive

yahoo_qa

• Dataset: yahoo_qa
• Size: 143,477 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 8 tokens mean: 29.44 tokens max: 32 tokens	min: 9 tokens mean: 31.05 tokens max: 32 tokens

• Samples:

  query	document
  I have to meet up with someone whose last name is Kasprazck tomorrow and I dont wanna offend her by sayin her name wrong. Can u please write out how its pronounced if you know or how you think? Thanks xoxoxoxooxo	People with surname like that are usually aware that people may not know how to pronounce it properly. It will not be a big issue (and I am sure it won't offend her at all) if you were to ask how to pronounce it. Just make sure you listen carefully THEN repeat it so you will likely remember it.
  All I want to know why is this allowed when there is so much of a danger to children that are online. I am in charge of her as of 3/20/2006 and she is nolonger with her Mother who got her started with this problum and I was under the imprestion that this account was canceled out but I went to use my computer and I found out that she had been online without my knowledge of it til today I can only give you the email address I dont have her password for the my space I do have the home address that may have been givin and the phone number and her true date of birth. My sister is the one who told the lie.	Your question is essentially "All I want to know why is this allowed...?"\n\nThe answer is, nobody allowed it but you. You made a computer accessible to someone who you do not wish it to be used by.\n\nIf you meant to ask "Why are minors allowed to set up email accounts?" then the answer is, "Because there is no way to ensure that the person on the client's end isn't minor."\n\nIf you want to have her MySpace account removed, there are protocols you can follow on the MySpace FAQ (frequently asked questions). However, it will be pointless to go through the trouble if she has access to the internet; your home, school, friends, the mall, Kinko's, etc.
  I am not asking you alter anything you already have in place,\nbut why not combine Biology, and Chemistry into Biochemistry, yes, that is what I am searching for.	Biology has traditionally consisted of botany, zoology and microbiology. Chemistry has consisted mainly of physical, organic, analytical and biochemistry. Until relatively recently those divisions have held up reasonably well. Now there is a whole new world of chemical and physical biology and biochemistry and biophysics. To throw all of biology and chemistry into biochemistry would be a misnomer for many of the parts of both. To add biochemistry or chemical biology into one pot might be a good idea to catch the questions in the area betwen

• Loss: pylate.losses.cached_contrastive.CachedContrastive

yahoo_title_question

• Dataset: yahoo_title_question
• Size: 213,320 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 7 tokens mean: 17.92 tokens max: 32 tokens	min: 7 tokens mean: 28.89 tokens max: 32 tokens

• Samples:

  query	document
  1:03 s for 100 meter freestyle race 10 year old female category. does this time rank high in the USA Swimming?	My daugter just went 1:03 in 100meter fresstyle how does this compare to the best 10 year olds in the world or USA?
  Why doesnt people believe that a mental illness or condition is a real medical probllem?	It seems that unless people can see a "broken arm", a "bleeding wound", a "cancer diagnosis", "asthma" , "arthiritis" (and many more lables out there) a mental condition is less inmportant as the above. There are so many people that do not understand that it is real...it is a struggle everyday to just get to the end of the day. You are ridiculed for you behavior as irresponsible or inconsiderate. You get the picture. IT IS AS REAL AS CANCER OR AIDS OR ANY OTHER UNCUREABLE ILLNESS!!
  Why do you think people attand college or university?	people attand college or university for many different reasons,e.x.new experiences, career preparation, increased knowledge...

• Loss: pylate.losses.cached_contrastive.CachedContrastive

ccnews

• Dataset: ccnews
• Size: 353,670 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 7 tokens mean: 17.75 tokens max: 32 tokens	min: 22 tokens mean: 31.98 tokens max: 32 tokens

• Samples:

  query	document
  California Senate Approves Raising Age to Buy Long Guns	SACRAMENTO (AP) — California would raise the age for buying rifles and shotguns from 18 to 21 and bar people from buying more than one long gun each month under a bill advancing in the Legislature. It’s been a frequently debated topic nationwide after a Florida high school shooting that killed 17 people. The Senate on Tuesday approved the measure by Democratic Sen. Anthony Portantino of La Canada Flintridge, sending it to the Assembly on a 23-10 vote. It extends age and purchase limits that currently apply only to handguns. Republican Sen. Jim Nielsen of Gerber says California should instead target criminal gangs and those with mental disabilities whom he said will obtain the guns no matter the legal limits. Walmart and Dick’s Sporting Goods previously announced age limits on gun sales.
  Mississippi officer fired after video of suspect being hit	JACKSON, Miss. (AP) – A Mississippi police officer has been fired after cellphone video showed him hitting a handcuffed suspect. A Jackson Police Department news release says officer Justin Roberts was fired Monday by Chief Lee Vance. The release says the suspect was hit Saturday; Vance started an internal affairs investigation after the video surfaced Sunday. The identity of the handcuffed man was not released. It was not immediately clear whether Roberts can appeal his firing. The Associated Press tried to leave a message for Roberts at the Jackson Police Department, but department spokesman Commander Tyree Jones says he does not have a way to reach the fired officer. Jones says both Roberts and the handcuffed suspect are African-American. Share this: Facebook LinkedIn Twitter Google Like this: Like Loading...
  Sir Cameron Mackintosh Discusses Newest Incarnation of MISS SAIGON	Sir Cameron Mackintosh, the man responsible for a nearly unrivaled number of influential theatrical productions, has mounted a new incarnation of Miss Saigon at the Birmingham Hippodrome. He recently spoke with Express and Star about the upcoming production. "This version is by far the best we have ever done," he says. "The world has sadly got worse, not better and we are indeed in gritty times and I think that is what has made the show feel even more contemporary than when it first came out nearly 30 years ago." The show features a new collection of designers and takes a grittier approach to the already hard-hitting content. Mackintosh says that even at the beginning the subject matter posed a monumental challenge in terms of transfer to the stage. When first speaking with Claude-Michel Schönberg and Alain Boublil he says, " the phrase I used then was 'doing this musical is like dancing on a razor blade'; you have to be utterly truthful and it has to deliver the power that only musica...

• Loss: pylate.losses.cached_contrastive.CachedContrastive

npr

• Dataset: npr
• Size: 365,075 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 7 tokens mean: 15.98 tokens max: 30 tokens	min: 16 tokens mean: 31.81 tokens max: 32 tokens

• Samples:

  query	document
  Chicago Sells City Relics in Online Auction	Pieces of Chicago's history and cultural experiences go up for bidding in a two-week auction beginning Thursday. The sale is an attempt to raise money for city arts and cultural programs, while also raising its profile. The "Great Chicago Fire Sale" is the first charitable eBay auction to be held by a municipality, and is being run by Chicago's Department of Cultural Affairs. Offerings include a dinner party prepared by Oprah Winfrey's chef, a chance to dye the river green on St. Patrick's Day, a cow statue from the city's 1999 Cows on Parade display and an authentic Playboy Bunny costume from the 1960s. NPR's David Schaper reports.
  Hear Code Orange's Darkly Catchy 'Bleeding In The Blur'	Code Orange could never be accused of going soft. Show up to any of the Pittsburgh band's shows and behold the cyclonic mosaic of moshing bodies moved by its nightmarishly chaotic hardcore. But there's always been an experimental underpinning to Code Orange that toys with noise and melody (and some '90s grunge). Forever, the band's upcoming third album, is among its most bruising works, with surprises throughout. But none are quite like this. "Bleeding In The Blur" certainly sets itself up to swarm, but the squealing feedback and Jami Morgan's thunderous drums quickly turn the reins over to guitarist Reba Meyers. No pinch harmonics, no slamming breakdowns, (mostly) no throaty screams — this is a darkly catchy pop song that sounds as if it's been carved from obsidian. "Bleeding In The Blur" has the gloomy heft of Thrice and the unconventional hooks of Jawbox, with Meyers' dominating vocals out front. If you've ever wanted a heavier song by Adventures (the emo band featuring three-quarte...
  Ireland Is The Focus Of Investor Anxieties	Over the past two weeks, investors have dumped Irish government bonds over concerns about the country's economy and its banks. Irish officials have been reluctant to accept a bailout. But over the weekend, they held talks about the debt crisis with other members of the European Union.

• Loss: pylate.losses.cached_contrastive.CachedContrastive

eli5

• Dataset: eli5
• Size: 106,781 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 6 tokens mean: 21.01 tokens max: 32 tokens	min: 14 tokens mean: 31.77 tokens max: 32 tokens

• Samples:

  query	document
  How far did Genghis Khan influence spread? and did it help america?	It would be rather difficult for Ghenghis Khan to influence America very much, given that the United States didn't exist until about 500 years after his death. It would be over 200 years before Columbus made his first voyage in search of Asia. At the time the only contact between America and the rest of the world would have been the Norse expedition to Vinland, and that didn't exactly end well. It's possible someone who's more knowledgeable about the subject could point to some cultural shifts that would affect America but with half a millennium of separation, Ghenghis Khan's influence on the USA would be pretty minimal.
  How did Stephen Hawking talked even though he can't move a muscle? How did the computer knew what he wanted to say?	He used very subtle muscle movements to control the computer. The computer would go over a list of letters/words and Hawking would move his muscle whenever he wanted to choose the current letter or word. Towards the end of his life it would take him up to a minute per word. Any interview you see of him is either heavily edited to remove these long pauses, or his entire talk was pre-recorded (that's how he gave lectures).
  How would William the Conqueror's name have been said/written in Old Norman?	William the Conqueror by David Bates p. 33 (ISBN 978-0752429601) and Hanks and Hodges, Oxford Dictionary of First Names, Oxford University Press, 2nd edition ( ISBN 978-0-19-861060-1), p.276 list it as Williame (french spelling Guillaume), all the other sources I found were too unreliable.

• Loss: pylate.losses.cached_contrastive.CachedContrastive

cnn

• Dataset: cnn
• Size: 293,521 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 15 tokens mean: 31.85 tokens max: 32 tokens	min: 32 tokens mean: 32.0 tokens max: 32 tokens

• Samples:

  query	document
  Chan, is famous in the United States for such action movies as 'Rush Hour' and 'Rumble in the Bronx' He lashed out at the United States and blamed the country for the financial crisis that is sweeping the globe .	He may enjoy a Hollywood payday now and then, but that doesn't stop Jackie Chan from criticizing America. The martial arts star called the U..S the 'most corrupt' country in the world during a recent interview on a Hong Kong television show. 'If you talk about corruption, the entire world, the United States has no corruption?' Chan asked the host. Scroll down for video . Controversial: Chan, who's made millions in American films, called the country the most corrupt nation on the planet . Chan then referred to America as 'the most corrupt in the world.' 'Where does this Great . Breakdown (financial crisis) come from? It started exactly from the . world, the United States,' Chan told the interviewer. 'When I was interviewed in the U.S., people . asked me, I said the same thing. 'I said now that China has become . strong, everyone is making an issue of China,' continued the Rush Hour star. 'If our own countrymen . don't support our country, who will support our country? We know our . coun...
  A bus was carrying members of King family after 'Dream' speech ceremony . The bus and a car collided near Washington's Tidal Basin just off the National Mall . Reality star Omarosa Manigault said she was on the bus: 'We were very afraid' Mall Police say a person in the car taken to hospital; no report yet on bus passengers .	Washington (CNN) -- Family members of the Rev. Martin Luther King Jr. were involved in a bus accident Wednesday after the high-profile ceremony marking the 50th anniversary of King's "I Have a Dream" speech, police said. The bus and a car collided near Washington's Tidal Basin just off the National Mall where the ceremony was held, according to Park Police, who have jurisdiction over the Mall. They said a person in the car was injured and taken to a hospital but did not provide information on injuries to bus passengers. Several members of the King family were aboard the bus and had laid a wreath at the memorial to the civil rights leader, according to Omarosa Manigault, a reality television star who was aboard the bus. "We were very afraid," she told CNN. "There were children on the bus, seniors and everything. Everybody was thrown out of their seats." She said she hit her head in the accident. Obama: Because they marched, America changed . 9 things about MLK's speech and the March on ...
  Ofcom's chief executive said there had been a big change in tolerance levels . 35% of viewers think there is too much violence, down from 55% in 2008 . But there is less tolerance of language deemed as 'discriminatory' or unjust . Critics say British public has become 'desensitised' due to lax Ofcom laws .	Television viewers have become more tolerant of violence and swearing, the head of Ofcom has claimed. But the sexist or racist language of the 1970s is far less acceptable than it once was, research by the broadcasting regulator reveals. Ofcom’s chief executive Ed Richards, who is about to stand down after 11 years in the job, told MPs there has been a big change in tolerance levels in the past few decades. Ofcom chief says the British public has grown more tolerant - but still does not like discriminatory language on TV shows. Till Death Us Do Part, which frequently had lead character Alf Garnett making racist remarks . But critics argued the British public has simply become ‘desensitised’ to swearing after years of lax regulation by Ofcom. According to the regulator’s latest research, published in July, only 35 per cent of viewers think there is too much violence on TV, down from 55 per cent in 2008. Just 35 per cent think there is too much swearing, down from 53 per cent six years a...

• Loss: pylate.losses.cached_contrastive.CachedContrastive

stackexchange_duplicate_questions

• Dataset: stackexchange_duplicate_questions
• Size: 73,210 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 6 tokens mean: 15.69 tokens max: 32 tokens	min: 6 tokens mean: 15.42 tokens max: 32 tokens

• Samples:

  query	document
  Clone() vs Copy constructor- which is recommended in java	clone() vs copy constructor vs factory method?
  AES-128/192 safer than AES-256 in practice?	Is AES-256 weaker than 192 and 128 bit versions?
  How does this Java code which determines whether a String contains all unique characters work?	Explain the use of a bit vector for determining if all characters are unique

• Loss: pylate.losses.cached_contrastive.CachedContrastive

stackexchange_title_body

• Dataset: stackexchange_title_body
• Size: 80,695 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 15 tokens mean: 31.95 tokens max: 32 tokens	min: 26 tokens mean: 31.96 tokens max: 32 tokens

• Samples:

  query	document
  Allow linking to named anchors This is similar to , but I don't think it's a dupe. Markdown should support links that are just named anchors, like foo. I occasionally reference existing answers in comments or my own answers if I'm expanding on them, and currently I need to include the full URL to get Markdown to link it, which seems unnecessary. Just copying the answer's link is annoying because it's a different URL, so when users click it it loads a new page, even though it's actually the exact same page. To get around it I tend to take the current URL and splice in the #id of the answer I'm linking to, but Markdown should assume that if I just include the #id part	Support anchor names in posts I admit this feature request is probably somewhat limited in useful scope, but I'm throwing it out there anyway. Inspired by , and because I want to use it on , I'm requesting that name be supported on a tags in posts. On very long answers, such as the closing/migration guidance answer, this would allow direct linking to the specific closure reason. This would then allow us, when someone , to link directly to the appropriate reason and description thereof. I recognize the limited scope of this, however, I have seen other long answers that could stand to have that kind of "deep" linking ability as well. (The original incarnation of this post had either name or id, but preferenced name. Per Koper's answer, which I agree with, I took out the idea of supporting id, because Koper's right -- too dangerous.)
  Unable to reload same gif image, if used twice in a page I am using same gif image twice in a page. Both the images will be hidden initially. Based on certain criteria I am showing those gif images (when clicked on particular target one gif image will be shown at a time). I am unable to reload the gif image. See the attached plunker 1) <script> var img1 = document.getElementById("img1"); var img2 = document.getElementById("img2"); function toggle1() { if (document.getElementById('gif-1').style.display == "none") { document.getElementById('gif-1').src = ''; document.getElementById('gif-1').src = 'http://insightgraphicdesign.net/wp-content/uploads/2014/07/coke-responsive-logo.gif'; document.getElementById('gif-1').style.display = "block"; } else document.getElementById('gif-1').style.display = "none"; } function toggle2() { if (document.getElementById('gif-2').style.display == "...	how to clear or replace a cached image I know there are many ways to prevent image caching (such as via META tags), as well as a few nice tricks to ensure that the current version of an image is shown with every page load (such as image.jpg?x=timestamp), but is there any way to actually clear or replace an image in the browsers cache so that neither of the methods above are necessary? As an example, lets say there are 100 images on a page and that these images are named "01.jpg", "02.jpg", "03.jpg", etc. If image "42.jpg" is replaced, is there any way to replace it in the cache so that "42.jpg" will automatically display the new image on successive page loads? I can't use the META tag method, because I need everuthing that ISN"T replaced to remain cached, and I can't use the timestamp method, because I don't want ALL of the images to be reloaded every time the page loads. I've racked my brain and scoured the Internet for a way to do this (preferrably via javascript), but no luck. Any...
  Is it possible that there are more than 6 quark flavors/more than 3 generations? I thought that things like the top quark don't exist in nature because they're super unstable and we can only observe them after high-energy collisions (e.g. LHC) Is it possible to make even more massive quarks? Or is there a reason the limit is six?	Why do we think there are only three generations of fundamental particles? In the of particle physics, there are three generations of quarks (up/down, strange/charm, and top/bottom), along with three generations of leptons (electron, muon, and tau). All of these particles have been observed experimentally, and we don't seem to have seen anything new along these lines. A priori, this doesn't eliminate the possibility of a fourth generation, but the physicists I've spoken to do not think additional generations are likely. Question: What sort of theoretical or experimental reasons do we have for this limitation? One reason I heard from my officemate is that we haven't seen new neutrinos. Neutrinos seem to be light enough that if another generation's neutrino is too heavy to be detected, then the corresponding quarks would be massive enough that new physics might interfere with their existence. This suggests the question: is there a general rule relating neutrino masses to quark...

• Loss: pylate.losses.cached_contrastive.CachedContrastive

stackexchange_body_body

• Dataset: stackexchange_body_body
• Size: 65,689 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 16 tokens mean: 31.73 tokens max: 32 tokens	min: 9 tokens mean: 31.57 tokens max: 32 tokens

• Samples:

  query	document
  When I type "sudo apt-get update" I see HTTP protocol is used to fetch the updates . Why not HTTPS is used for more secure communication ?	Does apt-get use https or any kind of encryption? Is there a way to configure it to use it?
  If I have 4 identical* LEDs wired in parallel to a single resistor so that the overall current available is 30 mA, do I still run the risk of premature burnout? The LEDs peak forward current is 30 mA. *I know that LEDs from the same package may still have slight differences I thoroughly read through the answers here - - but it seems like the assumption would be that one would arrange a circuit so that the available current equals the total draw of the 4 LEDs, in my case 80 mA. The problem then would be that some would draw more than the peak. But, if I'm limiting the avaialble current to 30mA, is there still an issue? That would mean that ideally 7.5mA would be supplied to each LED. Obviously, based on the answer in the aforementioned link, it would not likely be even, but it shouldn't get to "dangerous levels". Follow-up: Based on the volt/amperage curve, it looks like I'd be seeing a ~0.1V drop. Will this significantly affect the brightness? Still pretty new to all this so m...	I'm trying to wire up 6 RGB LEDs in parallel, all controlled from a single source (well, three sources, one for each colour). The LEDs came supplied with resistors to limit the current of 270 Ohm for a 5v supply. The problem is, 6 LEDs x 3 colours = 18 resistors, which is a lot, and means I need a much bigger board and a lot more soldering. So, can I instead wire the LEDs in parallel with each other, with a single resistor protecting all six? (3 resistors in total, one for each colour). How do I calculate the value of that resistor? More details: The LEDs are being driven from a to supply a bit of current, which is in turn controlled by a Netduino providing a PWM signal on the three channels. . If I've correctly understood the data sheet they want 20mA of current, and forward voltages of 2, 3, 3 volts (for R,G and B respectively?). The supplied resistors were all 270 Ohm, so the channels may not be balanced quite right. For extra credit: I'm only using 3 of the transistors in my...
  I want to make a figure in Mathematica, export it as a PDF, edit/label it in Photoshop, and then add it as a figure in a TeX document. I would really like to have the font in the figure closely match the math mode stuff in the document. In the past I've made a PDF with TeX with just the labels I want and then pasted them all into the figures, but this is incredibly tedious. I found this post - - which says that the font in math mode is "Latin Modern Symbol" but this is not an option in Photoshop. Is there another font that looks close enough to math mode which is in photoshop? Thanks for any help!	I draw figures in Inkscape. When I label elements within the figures with variable names that I have used in the underlying TeX document, I would like them to look exactly the same as in the document. (e.g. l does not look the same as $l$) What is the name of the math mode font so I can select it correctly in the Inkscape font list? If the exact font should not be available, what is a similar looking font that is present on most systems?

• Loss: pylate.losses.cached_contrastive.CachedContrastive

sentence_compression

• Dataset: sentence_compression
• Size: 173,604 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 11 tokens mean: 29.14 tokens max: 32 tokens	min: 7 tokens mean: 12.69 tokens max: 31 tokens

• Samples:

  query	document
  Sedgebrook, a continuing care retirement community located in Lincolnshire, will host a free support group for caregivers who support aging loved ones.	Sedgebrook retirement community to host support group for caregivers
  Junction City Police said in a news release Saturday that several shots were fired at the narcotics detective around midnight as he conducted surveillance in an unmarked vehicle.	Shots fired at narcotics detective
  A SWAT team surrounded a home on Miller Avenue in South San Francisco Monday afternoon, according to authorities and neighbors.	SWAT team surrounds home in South San Francisco

• Loss: pylate.losses.cached_contrastive.CachedContrastive

wikihow

• Dataset: wikihow
• Size: 96,029 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 5 tokens mean: 10.12 tokens max: 24 tokens	min: 9 tokens mean: 31.32 tokens max: 32 tokens

• Samples:

  query	document
  Dry and Propagate Comfrey	This article will tell you how to dry and propagate comfrey.
  Add a Playlist Shortcut on Android	Adding a playlist shortcut to your home screen is a surefire way to add convenience in using your Android device. For daily commutes or morning jogs, this is a useful feature to be able to start playing your music in the quickest way possible.
  Add an Android App to Google Drive	Google drive is a social service that can be used to share with friends. You can use Google Drive on your Android to share Android apps.

• Loss: pylate.losses.cached_contrastive.CachedContrastive

altlex

• Dataset: altlex
• Size: 110,708 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 5 tokens mean: 27.16 tokens max: 32 tokens	min: 6 tokens mean: 25.51 tokens max: 32 tokens

• Samples:

  query	document
  Avery County is a county located in the U.S. state of North Carolina .	Avery County is a county in the U.S. state of North Carolina .
  There he studied piano with Mieczyslaw Horszowski and composition with Constant Vauclain , and switched majors from piano to composition .	He studied piano at the Curtis Institute of Music , with Mieczyslaw Horszowski and composition with Constant Vauclain .
  The ReachOut website includes testimonials from a school nurse in Tucson , Arizona and an elementary school principal of the Deer Valley Unified School District in Greater Phoenix .	The ReachOut website has leters from a school nurse in Tucson , Arizona and an elementary school principal of the Deer Valley Unified School District in Greater Phoenix which say good things about ReachOut .

• Loss: pylate.losses.cached_contrastive.CachedContrastive

quora

• Dataset: quora
• Size: 44,885 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 7 tokens mean: 14.7 tokens max: 32 tokens	min: 7 tokens mean: 14.55 tokens max: 32 tokens

• Samples:

  query	document
  Website traffic analytics will show statistics for "direct navigation" which includes both typed in URL's (domain +.com) in the URL bar, as well as those using bookmarks to get to a site. What is an estimate for the breakdown of each?	Website traffic analytics will show statistics for "direct navigation" which includes both typed in URL's (domain +.com) in the URL bar, as well as those using bookmarks to get to a site. Are there any statistics that show an estimated percentage of each rather than lumping them together?
  What are the most recognized flags in the world?	Which 10 flags are the most recognisable in the world?
  Can I deposit 500 & 1000 INR notes in my savings account multiple times on each banking day till 30/12/2016?	Can I deposit 500 & 1000 INR notes in my current account multiple times on each banking day till 30/12/2016?

• Loss: pylate.losses.cached_contrastive.CachedContrastive

simplewiki

• Dataset: simplewiki
• Size: 97,717 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 9 tokens mean: 26.14 tokens max: 32 tokens	min: 9 tokens mean: 27.9 tokens max: 32 tokens

• Samples:

  query	document
  Some of those rescued by the Nordnorge were taken to the Chilean Eduardo Frei Montalva Station on King George Island . Later they were flown by C-130 Hercules transport aircraft of the Chilean Air Force to Punta Arenas , Chile , in two separate flights on Saturday , November 24th , and Sunday , November 25th .	All of those rescued by Nordnorge were taken to the Chilean Frei Montalva Station on King George Island where they were subsequently airlifted by C-130 Hercules transport aircraft of the Chilean Air Force to Punta Arenas , Chile in two separate flights , one on Saturday , November 24 , and the other on Sunday , November 25 .
  The name of that province is Friesland . Leeuwarden is called Ljouwert in Frisian .	Leeuwarden ( , Stadsfries : Liwwadden , Frisian : Ljouwert , ) is the capital city of the Dutch province of Friesland .
  France has invested a lot in nuclear power . This made France the smallest producer of carbon dioxide among the seven most industrialised countries in the world .	France is the smallest emitter of carbon dioxide among the seven most industrialized countries in the world , due to its heavy investment in nuclear power .

• Loss: pylate.losses.cached_contrastive.CachedContrastive

squad

• Dataset: squad
• Size: 25,117 training samples
• Columns: query and document
• Approximate statistics based on the first 1000 samples:

  	query	document
  type	string	string
  details	min: 7 tokens mean: 15.81 tokens max: 32 tokens	min: 32 tokens mean: 32.0 tokens max: 32 tokens

• Samples:

  query	document
  What percentage of Italians spoke standard Italian when Italy was first unified?	During the Risorgimento, proponents of Italian republicanism and Italian nationalism, such as Alessandro Manzoni, stressed the importance of establishing a uniform national language in order to better create an Italian national identity. With the unification of Italy in the 1860s, standard Italian became the official national language of the new Italian state, while the various unofficial regional languages of Italy gradually became regarded as subordinate "dialects" to Italian, increasingly associated negatively with lack of education or provincialism. However, at the time of the Italian Unification, standard Italian still existed mainly as a literary language, and only 2.5% of Italy's population could speak standard Italian.
  What type of process is used to produce most paper used in paperback books?	Mechanical pulping yields almost a tonne of pulp per tonne of dry wood used, which is why mechanical pulps are sometimes referred to as "high yield" pulps. With almost twice the yield as chemical pulping, mechanical pulps is often cheaper. Mass-market paperback books and newspapers tend to use mechanical papers. Book publishers tend to use acid-free paper, made from fully bleached chemical pulps for hardback and trade paperback books.
  What do orthodox Jews express ambivalence towards?	Politically, Orthodox Jews, given their variety of movements and affiliations, tend not to conform easily to the standard left-right political spectrum, with one of the key differences between the movements stemming from the groups' attitudes to Zionism. Generally speaking, of the three key strands of Orthodox Judaism, Haredi Orthodox and Hasidic Orthodox Jews are at best ambivalent towards the ideology of Zionism and the creation of the State of Israel, and there are many groups and organisations who are outspokenly anti-Zionistic, seeing the ideology of Zionism as diametrically opposed to the teaching of the Torah, and the Zionist administration of the State of Israel, with its emphasis on militarism and nationalism, as destructive of the Judaic way of life.

• Loss: pylate.losses.cached_contrastive.CachedContrastive

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 8192
• per_device_eval_batch_size: 8192
• learning_rate: 0.0001
• num_train_epochs: 1.0
• bf16: True
• dataloader_num_workers: 8
• accelerator_config: {'split_batches': True, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 8192
• per_device_eval_batch_size: 8192
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 0.0001
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1.0
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• bf16: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: True
• dataloader_num_workers: 8
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': True, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• hub_revision: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: no
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: True
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	NanoClimateFEVER_MaxSim_ndcg@10	NanoDBPedia_MaxSim_ndcg@10	NanoFEVER_MaxSim_ndcg@10	NanoFiQA2018_MaxSim_ndcg@10	NanoHotpotQA_MaxSim_ndcg@10	NanoMSMARCO_MaxSim_ndcg@10	NanoNFCorpus_MaxSim_ndcg@10	NanoNQ_MaxSim_ndcg@10	NanoQuoraRetrieval_MaxSim_ndcg@10	NanoSCIDOCS_MaxSim_ndcg@10	NanoArguAna_MaxSim_ndcg@10	NanoSciFact_MaxSim_ndcg@10	NanoTouche2020_MaxSim_ndcg@10	NanoBEIR_mean_MaxSim_ndcg@10
0.0017	50	282.9698	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0034	100	55.1656	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0051	150	34.5182	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0069	200	24.3272	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0086	250	15.1815	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0103	300	13.4824	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0120	350	12.7103	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0137	400	13.1518	0.3096	0.6016	0.8583	0.4606	0.7464	0.5667	0.3173	0.5488	0.9386	0.3734	0.5440	0.7260	0.4898	0.5755
0.0154	450	13.8519	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0171	500	23.4039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0189	550	12.6051	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0206	600	11.7196	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0223	650	11.2775	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0240	700	12.0364	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0257	750	12.8098	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0274	800	14.4034	0.3035	0.6189	0.8919	0.4695	0.7273	0.5854	0.3157	0.6237	0.9348	0.3919	0.5477	0.7425	0.4995	0.5887
0.0291	850	13.5692	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0309	900	5.5937	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0326	950	15.6469	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0343	1000	8.3445	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0360	1050	11.4676	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0377	1100	10.5587	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0394	1150	7.2106	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0412	1200	8.8266	0.3133	0.6045	0.8674	0.4878	0.7475	0.5680	0.3189	0.6143	0.9324	0.3948	0.5608	0.7609	0.4835	0.5888
0.0429	1250	8.5842	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0446	1300	8.1969	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0463	1350	12.9375	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0480	1400	9.4994	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0497	1450	10.5506	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0514	1500	11.8381	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0532	1550	12.0144	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0549	1600	8.6893	0.2995	0.6221	0.8902	0.4924	0.7664	0.5785	0.3224	0.5905	0.9487	0.3862	0.5589	0.7558	0.5039	0.5935
0.0566	1650	7.0217	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0583	1700	9.6189	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0600	1750	6.7589	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0617	1800	10.8403	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0634	1850	8.7685	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0652	1900	7.0266	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0669	1950	9.4464	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0686	2000	7.2878	0.3267	0.6036	0.8853	0.4733	0.7403	0.5734	0.3350	0.6237	0.9467	0.3907	0.5429	0.7561	0.4969	0.5919
0.0703	2050	4.9689	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0720	2100	8.321	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0737	2150	7.4099	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0754	2200	8.0318	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0772	2250	12.6529	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0789	2300	6.8628	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0806	2350	5.2555	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0823	2400	14.1963	0.3020	0.6050	0.8891	0.4646	0.7835	0.6052	0.3073	0.6392	0.9545	0.3821	0.5615	0.7409	0.5019	0.5951
0.0840	2450	9.6913	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0857	2500	8.9171	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0874	2550	9.2613	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0892	2600	13.9137	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0909	2650	8.6853	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0926	2700	4.6838	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0943	2750	12.1341	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0960	2800	9.7981	0.3173	0.6054	0.8900	0.4801	0.7736	0.5511	0.3105	0.6364	0.9518	0.3947	0.5613	0.7744	0.4919	0.5953
0.0977	2850	9.6777	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0995	2900	5.8349	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1012	2950	7.4624	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1029	3000	7.6541	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1046	3050	10.7707	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1063	3100	2.7293	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1080	3150	8.5695	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1097	3200	5.2242	0.3085	0.6283	0.8607	0.4949	0.7830	0.6213	0.3163	0.6441	0.9494	0.3871	0.5462	0.7790	0.5177	0.6028
0.1115	3250	4.3936	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1132	3300	7.5059	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1149	3350	7.9749	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1166	3400	9.7115	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1183	3450	7.1531	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1200	3500	5.5057	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1217	3550	8.0994	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1235	3600	7.8927	0.3075	0.6191	0.8953	0.4745	0.7631	0.5712	0.3361	0.6617	0.9388	0.4074	0.5891	0.7550	0.5123	0.6024
0.1252	3650	8.6786	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1269	3700	5.2801	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1286	3750	7.3722	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1303	3800	6.1661	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1320	3850	11.8176	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1337	3900	8.3136	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1355	3950	3.5611	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1372	4000	5.333	0.3022	0.6168	0.9035	0.4893	0.7588	0.5675	0.3291	0.6716	0.9396	0.3948	0.5622	0.7402	0.5081	0.5987
0.1389	4050	8.8173	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1406	4100	5.4447	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1423	4150	8.8454	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1440	4200	9.6296	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1457	4250	7.9232	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1475	4300	6.87	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1492	4350	6.8799	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1509	4400	9.3422	0.3190	0.6173	0.9027	0.4900	0.7954	0.5924	0.3314	0.6221	0.9368	0.4016	0.5646	0.7703	0.5166	0.6046
0.1526	4450	5.0552	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1543	4500	7.1861	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1560	4550	12.7564	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1578	4600	4.0789	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1595	4650	6.0549	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1612	4700	4.7162	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1629	4750	7.658	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1646	4800	5.7021	0.3056	0.6252	0.9081	0.4909	0.8005	0.5775	0.3320	0.6584	0.9410	0.3981	0.5687	0.7931	0.5150	0.6088
0.1663	4850	7.0474	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1680	4900	3.0414	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1698	4950	8.6702	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1715	5000	5.7714	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1732	5050	7.7096	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1749	5100	9.3515	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1766	5150	10.0721	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1783	5200	3.7851	0.3133	0.6273	0.9000	0.4621	0.7855	0.5906	0.3378	0.6689	0.9413	0.3924	0.5730	0.7707	0.4961	0.6045
0.1800	5250	9.203	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1818	5300	8.3322	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1835	5350	3.0129	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1852	5400	5.418	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1869	5450	11.4444	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1886	5500	5.323	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1903	5550	10.4423	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1920	5600	3.6124	0.3101	0.6244	0.8879	0.4869	0.7784	0.5861	0.3155	0.6604	0.9261	0.4099	0.5711	0.7686	0.4897	0.6012
0.1938	5650	3.9251	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1955	5700	5.1445	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1972	5750	4.7304	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1989	5800	7.5384	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2006	5850	5.7073	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2023	5900	5.3039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2040	5950	6.3666	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2058	6000	9.3195	0.3041	0.6154	0.8894	0.4902	0.7809	0.6249	0.3344	0.6605	0.9511	0.4063	0.5674	0.7546	0.4971	0.6059
0.2075	6050	10.8783	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2092	6100	6.9212	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2109	6150	5.9612	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2126	6200	7.5544	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2143	6250	4.6671	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2160	6300	5.1746	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2178	6350	2.1954	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2195	6400	9.0396	0.2855	0.6100	0.8887	0.4823	0.7672	0.6084	0.3146	0.6820	0.9382	0.4047	0.5473	0.7369	0.5021	0.5975
0.2212	6450	5.3861	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2229	6500	1.8836	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2246	6550	6.5989	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2263	6600	3.9696	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2281	6650	4.9495	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2298	6700	4.0348	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2315	6750	3.4135	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2332	6800	8.2786	0.2993	0.6309	0.8768	0.5079	0.7670	0.5952	0.3415	0.6822	0.9337	0.4033	0.5736	0.7483	0.5126	0.6056
0.2349	6850	5.945	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2366	6900	6.3681	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2383	6950	8.4549	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2401	7000	6.3826	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2418	7050	7.0919	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2435	7100	9.1379	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2452	7150	7.8642	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2469	7200	6.445	0.2850	0.6062	0.8860	0.4899	0.7951	0.6007	0.3322	0.6962	0.9398	0.3947	0.5505	0.7423	0.4895	0.6006
0.2486	7250	4.2054	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2503	7300	4.7528	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2521	7350	12.8999	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2538	7400	5.0565	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2555	7450	7.2818	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2572	7500	3.176	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2589	7550	7.2019	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2606	7600	3.5032	0.2949	0.6097	0.9010	0.4952	0.7692	0.6118	0.3405	0.6805	0.9374	0.4091	0.5538	0.7563	0.5094	0.6053
0.2623	7650	7.1541	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2641	7700	8.4615	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2658	7750	4.104	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2675	7800	4.1648	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2692	7850	3.8237	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2709	7900	6.4739	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2726	7950	9.5013	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2743	8000	6.5267	0.3191	0.6245	0.9186	0.4975	0.7940	0.6012	0.3497	0.6773	0.9344	0.4170	0.5381	0.7567	0.4999	0.6098
0.2761	8050	7.8327	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2778	8100	6.2043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2795	8150	7.385	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2812	8200	5.6921	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2829	8250	5.9292	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2846	8300	7.5891	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2864	8350	4.6461	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2881	8400	5.7636	0.2775	0.6182	0.8759	0.4859	0.7891	0.5811	0.3514	0.6533	0.9223	0.4115	0.5405	0.7720	0.5067	0.5989
0.2898	8450	8.2547	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2915	8500	3.1056	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2932	8550	4.7967	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2949	8600	8.3713	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2966	8650	6.2276	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2984	8700	4.8687	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3001	8750	3.2732	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3018	8800	6.3277	0.2987	0.6365	0.9186	0.4915	0.7986	0.5765	0.3321	0.6554	0.9342	0.4108	0.5463	0.7501	0.5170	0.6051
0.3035	8850	4.7854	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3052	8900	4.3765	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3069	8950	9.4066	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3086	9000	5.0629	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3104	9050	3.613	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3121	9100	9.9824	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3138	9150	8.3835	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3155	9200	4.6594	0.3153	0.6254	0.8972	0.5147	0.7719	0.5845	0.3435	0.6798	0.9314	0.4105	0.5553	0.7752	0.5130	0.6090
0.3172	9250	3.164	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3189	9300	3.0871	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3206	9350	5.1658	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3224	9400	5.7431	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3241	9450	10.4233	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3258	9500	8.4279	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3275	9550	8.9253	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3292	9600	7.1729	0.2858	0.6358	0.9103	0.4994	0.7873	0.5993	0.3400	0.6913	0.9304	0.4131	0.5615	0.7696	0.5038	0.6098
0.3309	9650	5.9275	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3326	9700	6.924	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3344	9750	9.2746	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3361	9800	5.254	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3378	9850	5.6372	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3395	9900	3.6653	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3412	9950	5.09	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3429	10000	2.4444	0.2936	0.6200	0.9084	0.4866	0.7854	0.6221	0.3356	0.6505	0.9492	0.4036	0.5764	0.7828	0.4933	0.6083
0.3447	10050	7.1372	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3464	10100	3.8513	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3481	10150	4.5267	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3498	10200	4.1433	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3515	10250	4.6686	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3532	10300	11.4151	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3549	10350	4.4418	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3567	10400	2.4961	0.3038	0.6377	0.9333	0.4848	0.7801	0.6169	0.3373	0.6611	0.9327	0.4100	0.5661	0.7646	0.5158	0.6111
0.3584	10450	6.1801	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3601	10500	8.6687	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3618	10550	5.6287	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3635	10600	5.4743	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3652	10650	6.7785	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3669	10700	4.4236	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3687	10750	5.8139	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3704	10800	8.2798	0.3016	0.6423	0.9097	0.4930	0.7774	0.5968	0.3612	0.6653	0.9324	0.4160	0.5454	0.7584	0.5038	0.6080
0.3721	10850	4.0158	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3738	10900	4.7021	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3755	10950	5.921	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3772	11000	3.304	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3789	11050	4.2674	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3807	11100	8.2767	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3824	11150	4.6367	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3841	11200	7.2655	0.2875	0.6444	0.9129	0.5053	0.8100	0.6215	0.3384	0.6545	0.9229	0.4116	0.5693	0.7583	0.5091	0.6112
0.3858	11250	8.0162	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3875	11300	1.9676	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3892	11350	2.7514	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3909	11400	4.1716	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3927	11450	8.9283	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3944	11500	4.5011	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3961	11550	3.4287	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3978	11600	6.551	0.3007	0.6240	0.8928	0.4733	0.7960	0.6299	0.3478	0.6849	0.9290	0.4080	0.5713	0.7542	0.5139	0.6097
0.3995	11650	8.8618	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4012	11700	3.2635	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4029	11750	5.771	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4047	11800	2.6087	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4064	11850	3.95	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4081	11900	4.5284	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4098	11950	4.5608	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4115	12000	9.7888	0.3020	0.6008	0.8858	0.4887	0.7791	0.6145	0.3634	0.6636	0.9332	0.4234	0.5521	0.7701	0.5046	0.6062
0.4132	12050	3.4363	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4150	12100	3.4266	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4167	12150	5.3757	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4184	12200	5.4381	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4201	12250	10.0749	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4218	12300	4.0014	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4235	12350	6.6097	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4252	12400	9.5719	0.2892	0.6150	0.8956	0.4878	0.7796	0.6390	0.3512	0.6534	0.9375	0.4221	0.5843	0.7552	0.5208	0.6100
0.4270	12450	3.2915	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4287	12500	5.1686	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4304	12550	5.296	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4321	12600	3.2917	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4338	12650	5.9393	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4355	12700	5.1413	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4372	12750	8.4962	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4390	12800	6.1012	0.3018	0.6164	0.9096	0.4898	0.8024	0.6311	0.3486	0.6645	0.9321	0.4049	0.5800	0.7552	0.5070	0.6110
0.4407	12850	4.4983	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4424	12900	6.5854	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4441	12950	10.2422	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4458	13000	5.8021	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4475	13050	4.6708	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4492	13100	4.4848	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4510	13150	2.449	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4527	13200	6.9588	0.2916	0.6149	0.9159	0.4847	0.7891	0.6179	0.3477	0.6664	0.9345	0.4203	0.5780	0.7723	0.5081	0.6109
0.4544	13250	8.8453	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4561	13300	4.3965	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4578	13350	5.0768	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4595	13400	7.0408	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4612	13450	3.6231	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4630	13500	3.7822	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4647	13550	8.4116	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4664	13600	4.1345	0.2966	0.6143	0.9425	0.4988	0.7819	0.6239	0.3389	0.6566	0.9285	0.4019	0.5651	0.7661	0.5168	0.6102
0.4681	13650	2.5318	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4698	13700	3.8869	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4715	13750	3.2607	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4733	13800	6.0514	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4750	13850	4.8758	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4767	13900	8.552	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4784	13950	4.2703	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4801	14000	3.5152	0.2763	0.6184	0.9208	0.5218	0.7610	0.6323	0.3410	0.6654	0.9370	0.4077	0.5517	0.7777	0.5082	0.6092
0.4818	14050	6.5297	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4835	14100	3.473	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4853	14150	9.5062	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4870	14200	5.1116	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4887	14250	5.2527	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4904	14300	4.1066	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4921	14350	2.4521	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4938	14400	6.9098	0.2900	0.6062	0.9326	0.5125	0.7781	0.6211	0.3273	0.6571	0.9299	0.4049	0.5677	0.7413	0.5066	0.6058
0.4955	14450	6.279	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4973	14500	2.5648	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4990	14550	4.3318	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5007	14600	5.5523	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5024	14650	8.6016	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5041	14700	5.1184	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5058	14750	3.8743	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5075	14800	2.3507	0.2850	0.6403	0.9042	0.4970	0.7943	0.6259	0.3313	0.6584	0.9350	0.3990	0.5626	0.7611	0.4894	0.6064
0.5093	14850	8.4885	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5110	14900	6.5251	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5127	14950	3.149	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5144	15000	5.2221	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5161	15050	2.2784	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5178	15100	6.0501	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5195	15150	3.8561	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5213	15200	2.9294	0.2896	0.6327	0.9143	0.5031	0.7838	0.6277	0.3383	0.6556	0.9342	0.4047	0.5741	0.7525	0.5195	0.6100
0.5230	15250	5.8455	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5247	15300	5.8723	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5264	15350	6.7928	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5281	15400	7.2912	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5298	15450	9.7424	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5316	15500	4.4311	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5333	15550	5.6671	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5350	15600	8.3659	0.3017	0.6140	0.9199	0.5242	0.8075	0.6304	0.3396	0.7000	0.9324	0.4119	0.5375	0.7628	0.5125	0.6149
0.5367	15650	3.3358	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5384	15700	5.3524	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5401	15750	5.8091	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5418	15800	7.6688	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5436	15850	3.7723	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5453	15900	8.3872	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5470	15950	5.2054	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5487	16000	4.9648	0.2931	0.6183	0.9171	0.4962	0.7889	0.6179	0.3339	0.6692	0.9392	0.4120	0.5577	0.7561	0.5013	0.6078
0.5504	16050	5.6397	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5521	16100	6.1621	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5538	16150	6.2562	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5556	16200	1.4896	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5573	16250	8.5106	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5590	16300	3.4925	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5607	16350	4.8838	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5624	16400	4.7842	0.2943	0.6078	0.9154	0.5215	0.7651	0.6171	0.3334	0.6521	0.9373	0.4158	0.5419	0.7596	0.5192	0.6062
0.5641	16450	2.3668	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5658	16500	6.3814	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5676	16550	4.7606	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5693	16600	3.7603	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5710	16650	4.2997	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5727	16700	4.5546	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5744	16750	1.352	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5761	16800	2.6079	0.2913	0.6171	0.9384	0.5153	0.7864	0.6295	0.3380	0.6886	0.9410	0.4153	0.5424	0.7576	0.5063	0.6129
0.5778	16850	2.833	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5796	16900	5.4264	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5813	16950	5.0618	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5830	17000	1.8122	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5847	17050	4.8089	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5864	17100	3.9175	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5881	17150	3.5493	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5898	17200	5.5462	0.3046	0.6075	0.9182	0.5006	0.7926	0.6060	0.3305	0.6878	0.9399	0.4205	0.5452	0.7677	0.5032	0.6096
0.5916	17250	4.3846	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5933	17300	4.0412	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5950	17350	7.2878	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5967	17400	5.3043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5984	17450	2.8274	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6001	17500	7.441	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6019	17550	5.0948	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6036	17600	2.6579	0.2910	0.6211	0.9058	0.5193	0.7965	0.5949	0.3370	0.6889	0.9420	0.4094	0.5272	0.7481	0.5039	0.6066
0.6053	17650	5.7531	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6070	17700	7.8901	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6087	17750	6.2321	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6104	17800	2.9725	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6121	17850	2.9978	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6139	17900	7.4669	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6156	17950	5.5275	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6173	18000	3.4557	0.2966	0.6261	0.9358	0.4946	0.8147	0.6263	0.3326	0.6843	0.9432	0.4218	0.5524	0.7608	0.5259	0.6166
0.6190	18050	2.9255	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6207	18100	6.367	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6224	18150	3.0032	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6241	18200	3.9555	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6259	18250	4.9943	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6276	18300	3.7715	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6293	18350	4.8653	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6310	18400	3.7575	0.3117	0.6151	0.9239	0.5126	0.7887	0.6148	0.3478	0.6691	0.9421	0.4158	0.5549	0.7872	0.5075	0.6147
0.6327	18450	5.0127	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6344	18500	4.7489	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6361	18550	4.7513	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6379	18600	6.0124	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6396	18650	6.2351	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6413	18700	4.3376	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6430	18750	3.2709	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6447	18800	4.947	0.3143	0.6110	0.9227	0.4948	0.8041	0.6034	0.3254	0.6837	0.9453	0.4113	0.5591	0.7689	0.5225	0.6128
0.6464	18850	5.1706	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6481	18900	6.0173	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6499	18950	7.6975	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6516	19000	2.3972	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6533	19050	6.6009	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6550	19100	3.617	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6567	19150	2.8273	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6584	19200	3.6362	0.2882	0.6083	0.9186	0.5065	0.8208	0.6041	0.3333	0.6848	0.9424	0.4095	0.5755	0.7576	0.5247	0.6134
0.6602	19250	7.6529	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6619	19300	9.4309	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6636	19350	3.238	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6653	19400	5.8617	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6670	19450	4.9989	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6687	19500	4.3933	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6704	19550	7.4218	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6722	19600	2.1687	0.2855	0.6273	0.9168	0.5110	0.7945	0.6185	0.3417	0.6839	0.9517	0.4239	0.5720	0.7790	0.5188	0.6173
0.6739	19650	4.6332	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6756	19700	10.165	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6773	19750	7.286	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6790	19800	3.8391	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6807	19850	7.0988	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6824	19900	6.7	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6842	19950	3.8806	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6859	20000	6.6851	0.2903	0.6124	0.9297	0.5144	0.7679	0.6095	0.3413	0.6845	0.9411	0.4204	0.5711	0.7344	0.5153	0.6102
0.6876	20050	2.663	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6893	20100	5.2286	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6910	20150	3.4183	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6927	20200	2.224	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6944	20250	4.587	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6962	20300	2.9378	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6979	20350	3.345	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6996	20400	3.6517	0.2839	0.6216	0.9324	0.5112	0.7897	0.6317	0.3302	0.6788	0.9403	0.4155	0.5694	0.7854	0.5233	0.6164
0.7013	20450	6.0092	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7030	20500	8.8043	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7047	20550	4.6224	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7064	20600	7.9055	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7082	20650	3.7945	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7099	20700	7.0954	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7116	20750	4.9394	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7133	20800	4.7641	0.2907	0.6184	0.9371	0.5086	0.7672	0.6274	0.3280	0.6680	0.9423	0.4161	0.5622	0.7691	0.5123	0.6113
0.7150	20850	2.9915	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7167	20900	5.6771	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7184	20950	8.3292	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7202	21000	7.1981	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7219	21050	4.1615	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7236	21100	4.2739	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7253	21150	5.811	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7270	21200	6.1299	0.2998	0.6196	0.9345	0.5107	0.7600	0.6259	0.3410	0.6825	0.9416	0.4301	0.5549	0.7586	0.5093	0.6130
0.7287	21250	4.6447	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7305	21300	4.8578	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7322	21350	5.5121	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7339	21400	6.3301	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7356	21450	4.4325	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7373	21500	2.5755	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7390	21550	4.8057	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7407	21600	5.5245	0.3072	0.6124	0.9407	0.5032	0.7688	0.6048	0.3390	0.6502	0.9402	0.4237	0.5834	0.7731	0.5164	0.6125
0.7425	21650	4.972	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7442	21700	6.5188	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7459	21750	7.2309	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7476	21800	9.9937	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7493	21850	7.1234	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7510	21900	5.6781	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7527	21950	4.2348	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7545	22000	6.8552	0.3106	0.6127	0.9347	0.5092	0.7764	0.6334	0.3412	0.6594	0.9424	0.4226	0.5797	0.7724	0.5161	0.6162
0.7562	22050	2.6148	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7579	22100	6.8239	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7596	22150	5.9068	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7613	22200	2.3372	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7630	22250	13.3205	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7647	22300	2.2775	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7665	22350	3.5904	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7682	22400	6.1356	0.2926	0.6227	0.9327	0.5106	0.8098	0.6305	0.3368	0.6746	0.9489	0.4052	0.5770	0.7750	0.5081	0.6173
0.7699	22450	2.0879	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7716	22500	3.4941	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7733	22550	5.7535	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7750	22600	6.1596	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7767	22650	4.2398	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7785	22700	6.1685	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7802	22750	5.9098	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7819	22800	2.1767	0.2952	0.6352	0.9356	0.5188	0.8080	0.6319	0.3282	0.6808	0.9453	0.4153	0.5744	0.7672	0.5105	0.6189
0.7836	22850	5.9796	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7853	22900	4.1611	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7870	22950	6.3994	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7888	23000	1.8667	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7905	23050	5.6744	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7922	23100	3.5483	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7939	23150	4.2125	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7956	23200	5.2505	0.2868	0.6262	0.9248	0.5005	0.7968	0.6160	0.3349	0.6798	0.9455	0.4205	0.5726	0.7597	0.5134	0.6137
0.7973	23250	5.8586	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7990	23300	4.3494	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8008	23350	4.7328	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8025	23400	4.5992	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8042	23450	5.6783	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8059	23500	8.2855	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8076	23550	3.7126	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8093	23600	3.9131	0.2929	0.6148	0.9424	0.5036	0.7792	0.6242	0.3394	0.6859	0.9478	0.4166	0.5532	0.7643	0.5157	0.6139
0.8110	23650	5.689	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8128	23700	4.408	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8145	23750	5.375	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8162	23800	4.3793	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8179	23850	5.8089	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8196	23900	3.8131	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8213	23950	4.2499	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8230	24000	6.775	0.2819	0.6171	0.9357	0.5044	0.8019	0.6284	0.3416	0.6656	0.9412	0.4167	0.5569	0.7683	0.5168	0.6136
0.8248	24050	4.3352	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8265	24100	5.7273	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8282	24150	4.4085	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8299	24200	1.3667	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8316	24250	3.847	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8333	24300	4.6927	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8350	24350	4.7152	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8368	24400	7.374	0.3067	0.6179	0.9357	0.4976	0.8144	0.6238	0.3469	0.6925	0.9419	0.4134	0.5582	0.7801	0.5152	0.6188
0.8385	24450	4.4521	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8402	24500	2.216	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8419	24550	5.7958	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8436	24600	5.4267	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8453	24650	6.8011	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8471	24700	4.8741	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8488	24750	4.8045	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8505	24800	6.4177	0.2947	0.6151	0.9267	0.5011	0.7922	0.6183	0.3473	0.6905	0.9375	0.4131	0.5602	0.7792	0.5112	0.6144
0.8522	24850	3.6307	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8539	24900	3.5483	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8556	24950	2.7291	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8573	25000	3.1585	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8591	25050	10.0506	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8608	25100	7.6331	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8625	25150	1.2907	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8642	25200	5.3677	0.2879	0.6286	0.9328	0.4841	0.8025	0.6373	0.3360	0.6878	0.9384	0.4082	0.5856	0.7710	0.5114	0.6163
0.8659	25250	4.1432	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8676	25300	5.489	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8693	25350	5.8637	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8711	25400	2.2737	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8728	25450	9.846	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8745	25500	8.5132	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8762	25550	5.3678	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8779	25600	1.843	0.2892	0.6138	0.9222	0.5019	0.7968	0.6331	0.3358	0.6859	0.9435	0.4092	0.5765	0.7588	0.5090	0.6135
0.8796	25650	1.2667	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8813	25700	5.6289	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8831	25750	3.738	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8848	25800	4.4239	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8865	25850	4.7863	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8882	25900	3.2807	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8899	25950	2.3479	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8916	26000	3.5992	0.2924	0.6136	0.9280	0.5070	0.7869	0.6245	0.3378	0.6729	0.9410	0.4187	0.5529	0.7617	0.5141	0.6116
0.8933	26050	4.4068	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8951	26100	3.1816	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8968	26150	2.6621	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8985	26200	5.0237	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9002	26250	2.6081	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9019	26300	2.0825	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9036	26350	7.8321	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9053	26400	2.0112	0.2862	0.6148	0.9278	0.5116	0.8011	0.6255	0.3463	0.6559	0.9476	0.4094	0.5575	0.7659	0.5110	0.6124
0.9071	26450	8.6652	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9088	26500	4.1705	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9105	26550	5.0978	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9122	26600	3.2204	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9139	26650	8.7926	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9156	26700	3.5022	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9174	26750	3.4217	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9191	26800	6.0103	0.2982	0.6117	0.9307	0.5045	0.7936	0.6315	0.3406	0.6730	0.9405	0.4103	0.5764	0.7562	0.5103	0.6137
0.9208	26850	1.9632	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9225	26900	4.6666	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9242	26950	4.8264	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9259	27000	6.6911	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9276	27050	6.6328	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9294	27100	7.8961	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9311	27150	3.4175	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9328	27200	4.0392	0.2972	0.6076	0.9278	0.5043	0.7946	0.6339	0.3471	0.6791	0.9385	0.4193	0.5611	0.7649	0.5100	0.6143
0.9345	27250	5.469	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9362	27300	4.3261	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9379	27350	3.5746	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9396	27400	2.0889	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9414	27450	4.5245	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9431	27500	4.6485	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9448	27550	4.319	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9465	27600	3.3118	0.2991	0.6045	0.9209	0.4941	0.7906	0.6352	0.3387	0.6814	0.9414	0.4116	0.5566	0.7699	0.5063	0.6116
0.9482	27650	6.6937	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9499	27700	7.0913	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9516	27750	3.4734	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9534	27800	4.6161	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9551	27850	4.2888	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9568	27900	2.4579	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9585	27950	3.8057	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9602	28000	4.6289	0.2955	0.6126	0.9357	0.5076	0.7894	0.6211	0.3427	0.6835	0.9403	0.4135	0.5632	0.7706	0.5129	0.6145
0.9619	28050	5.543	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9636	28100	3.8364	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9654	28150	5.6743	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9671	28200	4.8397	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9688	28250	4.1642	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9705	28300	3.9614	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9722	28350	1.1594	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9739	28400	3.8841	0.2974	0.6122	0.9357	0.5134	0.7816	0.6286	0.3448	0.6850	0.9396	0.4142	0.5676	0.7615	0.5129	0.6150
0.9757	28450	1.478	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9774	28500	4.3356	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9791	28550	3.208	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9808	28600	6.542	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9825	28650	9.605	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9842	28700	6.9322	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9859	28750	5.7695	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9877	28800	8.4689	0.2944	0.6143	0.9431	0.5133	0.7870	0.6272	0.3477	0.6845	0.9423	0.4168	0.5579	0.7623	0.5090	0.6154
0.9894	28850	4.2508	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9911	28900	3.0545	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9928	28950	6.0077	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9945	29000	2.1921	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9962	29050	4.5755	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9979	29100	6.4715	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9997	29150	4.9482	-	-	-	-	-	-	-	-	-	-	-	-	-	-

Framework Versions

• Python: 3.12.12
• Sentence Transformers: 5.3.0
• PyLate: 1.4.0
• Transformers: 4.57.0
• PyTorch: 2.9.1
• Accelerate: 1.13.0
• Datasets: 4.8.4
• Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084"
}

PyLate

@inproceedings{DBLP:conf/cikm/ChaffinS25,
  author       = {Antoine Chaffin and
                  Rapha{"{e}}l Sourty},
  editor       = {Meeyoung Cha and
                  Chanyoung Park and
                  Noseong Park and
                  Carl Yang and
                  Senjuti Basu Roy and
                  Jessie Li and
                  Jaap Kamps and
                  Kijung Shin and
                  Bryan Hooi and
                  Lifang He},
  title        = {PyLate: Flexible Training and Retrieval for Late Interaction Models},
  booktitle    = {Proceedings of the 34th {ACM} International Conference on Information
                  and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November
                  10-14, 2025},
  pages        = {6334--6339},
  publisher    = {{ACM}},
  year         = {2025},
  url          = {https://github.com/lightonai/pylate},
  doi          = {10.1145/3746252.3761608},
}

CachedContrastive

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}