File size: 40,675 Bytes
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language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:300000
- loss:CachedMultipleNegativesRankingLoss
base_model: jhu-clsp/mmBERT-small
widget:
- source_sentence: where is henderson mn
sentences:
- Confidence votes 1.7K. Assuming we're talking about the `usual' 12 volt car battery'
the resting voltage should be around 11 to 11.5 volts. Under charge it's as high
as 15 volts as supplied from the alternator,and most cars won't start if the voltage
is under 10.5 to 11.5 volts. The term `12 volt battery' is what's referred to
as, `nominal' or `in name only' as a general reference and not meant to be an
accurate description.
- Henderson is a very small town of 1,000 people on the west bank of the Minnesota
River just south of the Minneapolis and Saint Paul metro area.
- Henderson, officially the City of Henderson, is an affluent city in Clark County,
Nevada, United States, about 16 miles southeast of Las Vegas. It is the second-largest
city in Nevada, after Las Vegas, with an estimated population of 292,969 in 2016.[2]
The city is part of the Las Vegas metropolitan area, which spans the entire Las
Vegas Valley. Henderson occupies the southeastern end of the valley, at an elevation
of approximately 1,330 feet (410 m).
- source_sentence: polytomy definition
sentences:
- Polytomy definition, the act or process of dividing into more than three parts.
See more.
- 'The name Loyalty has the following meaning: One who is faithful, loyal. It is
a male name, suitable for baby boys. Origins. The name Loyalty is very likely
a(n) English variant of the name Loyal. See other suggested English boy baby names.
You might also like to see the other variants of the name Loyal.'
- "Polysemy (/pÉ\x99Ë\x88lɪsɪmi/ or /Ë\x88pÉ\x92lɪsiË\x90mi/; from Greek: Ï\x80\
ολÏ\N-, poly-, many and Ï\x83á¿\x86μα, sêma, sign) is the capacity for a\
\ sign (such as a word, phrase, or symbol) to have multiple meanings (that is,\
\ multiple semes or sememes and thus multiple senses), usually related by contiguity\
\ of meaning within a semantic field."
- source_sentence: age group for juvenile arthritis
sentences:
- "Different Types of Juvenile Rheumatoid Arthritis. There are three kinds. Each\
\ type is based on the number of joints involved, the symptoms, and certain antibodies\
\ that may be in the blood. Four or fewer joints are involved. Doctors call this\
\ pauciarticular JRA. Itâ\x80\x99s the most common form. About half of all children\
\ with juvenile rheumatoid arthritis have this type. It usually affects large\
\ joints like the knees. Girls under age 8 are most likely to get it."
- Juvenile rheumatoid arthritis (JRA), often referred to by doctors today as juvenile
idiopathic arthritis (JIA), is a type of arthritis that causes joint inflammation
and stiffness for more than six weeks in a child aged 16 or younger. It affects
approximately 50,000 children in the United States.
- A depressant, or central depressant, is a drug that lowers neurotransmission levels,
which is to depress or reduce arousal or stimulation, in various areas of the
brain.Depressants are also occasionally referred to as downers as they lower the
level of arousal when taken.istilled (concentrated) alcoholic beverages, often
called hard liquor , roughly eight times more alcoholic than beer. An alcoholic
beverage is a drink that contains ethanol, an anesthetic that has been used as
a psychoactive drug for several millennia. Ethanol is the oldest recreational
drug still used by humans.
- source_sentence: what is besivance and durezol used for
sentences:
- Besivance is antibiotic eye drops, Prolensa is antiinflammatory eye drop and Durezol
is steroid eye drop. Besivance and Prolensa are need to be taken from 1-3 days
prior to surgery as a prophylaxis to prevent postoperative infection and inflammation
respectively. These eye drops can be administered after at least a gap of 5 minutes.
They are needed to be administered at least 4 times per day.
- .23 Acres Comfort, Kendall County, Texas. $399,500. This could be the most well
known building in Comfort with excellent all around visibility. Constructed in
the early 1930's and initially used as a bar it ...
- Duloxetine is used to treat major depressive disorder and general anxiety disorder.
Duloxetine is also used to treat fibromyalgia (a chronic pain disorder), or chronic
muscle or joint pain (such as low back pain and osteoarthritis pain). Duloxetine
is also used to treat pain caused by nerve damage in people with diabetes (diabetic
neuropathy).
- source_sentence: do bond funds pay dividends
sentences:
- If a cavity is causing the toothache, your dentist will fill the cavity or possibly
extract the tooth, if necessary. A root canal might be needed if the cause of
the toothache is determined to be an infection of the tooth's nerve. Bacteria
that have worked their way into the inner aspects of the tooth cause such an infection.
An antibiotic may be prescribed if there is fever or swelling of the jaw.
- "You would have $71,200 paying out $1,687 in annual dividends. That is about $4.62\
\ for working up in the morning. Interestingly enough, that 2.37% yield is at\
\ a low point because The Wellington Fund is a â\x80\x9Cbalanced fundâ\x80\x9D\
\ meaning that it holds a combination of stocks and bonds."
- A bond fund or debt fund is a fund that invests in bonds, or other debt securities.
Bond funds can be contrasted with stock funds and money funds. Bond funds typically
pay periodic dividends that include interest payments on the fund's underlying
securities plus periodic realized capital appreciation. Bond funds typically pay
higher dividends than CDs and money market accounts. Most bond funds pay out dividends
more frequently than individual bonds.
datasets:
- sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
co2_eq_emissions:
emissions: 68.14048860186945
energy_consumed: 0.2546146751831667
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.787
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: SentenceTransformer based on jhu-clsp/mmBERT-small
results:
- task:
type: triplet
name: Triplet
dataset:
name: msmarco co condenser eval triplet
type: msmarco-co-condenser-eval-triplet
metrics:
- type: cosine_accuracy
value: 0.6399999856948853
name: Cosine Accuracy
---
# SentenceTransformer based on jhu-clsp/mmBERT-small
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jhu-clsp/mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small) on the [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [jhu-clsp/mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small) <!-- at revision abc32620dd4f6ab06f5fbe905dc25f310618e09f -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'message_format': 'auto', 'architecture': 'ModernBertModel'})
(1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/mmBERT-small-msmarco-fa2-flattened-cmnrl")
# Run inference
sentences = [
'do bond funds pay dividends',
"A bond fund or debt fund is a fund that invests in bonds, or other debt securities. Bond funds can be contrasted with stock funds and money funds. Bond funds typically pay periodic dividends that include interest payments on the fund's underlying securities plus periodic realized capital appreciation. Bond funds typically pay higher dividends than CDs and money market accounts. Most bond funds pay out dividends more frequently than individual bonds.",
'You would have $71,200 paying out $1,687 in annual dividends. That is about $4.62 for working up in the morning. Interestingly enough, that 2.37% yield is at a low point because The Wellington Fund is a â\x80\x9cbalanced fundâ\x80\x9d meaning that it holds a combination of stocks and bonds.',
"If a cavity is causing the toothache, your dentist will fill the cavity or possibly extract the tooth, if necessary. A root canal might be needed if the cause of the toothache is determined to be an infection of the tooth's nerve. Bacteria that have worked their way into the inner aspects of the tooth cause such an infection. An antibiotic may be prescribed if there is fever or swelling of the jaw.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [4, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[0.6686, 0.4937, 0.2202]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Triplet
* Dataset: `msmarco-co-condenser-eval-triplet`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:---------|
| **cosine_accuracy** | **0.64** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
* Dataset: [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
* Size: 300,000 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 11 characters</li><li>mean: 32.46 characters</li><li>max: 140 characters</li></ul> | <ul><li>min: 67 characters</li><li>mean: 336.71 characters</li><li>max: 864 characters</li></ul> | <ul><li>min: 56 characters</li><li>mean: 339.96 characters</li><li>max: 900 characters</li></ul> |
* Samples:
| query | positive | negative |
|:---------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is the meaning of menu planning</code> | <code>Menu planning is the selection of a menu for an event. Such as picking out the dinner for your wedding or even a meal at a Birthday Party. Menu planning is when you are preparing a calendar of meals and you have to sit down and decide what meat and veggies you want to serve on each certain day.</code> | <code>Menu Costs. In economics, a menu cost is the cost to a firm resulting from changing its prices. The name stems from the cost of restaurants literally printing new menus, but economists use it to refer to the costs of changing nominal prices in general.</code> |
| <code>how old is brett butler</code> | <code>Brett Butler is 59 years old. To be more precise (and nerdy), the current age as of right now is 21564 days or (even more geeky) 517536 hours. That's a lot of hours!</code> | <code>Passed in: St. John's, Newfoundland and Labrador, Canada. Passed on: 16/07/2016. Published in the St. John's Telegram. Passed away suddenly at the Health Sciences Centre surrounded by his loving family, on July 16, 2016 Robert (Bobby) Joseph Butler, age 52 years. Predeceased by his special aunt Geri Murrin and uncle Mike Mchugh; grandparents Joe and Margaret Murrin and Jack and Theresa Butler.</code> |
| <code>when was the last navajo treaty sign?</code> | <code>In Executive Session, Senate of the United States, July 25, 1868. Resolved, (two-thirds of the senators present concurring,) That the Senate advise and consent to the ratification of the treaty between the United States and the Navajo Indians, concluded at Fort Sumner, New Mexico, on the first day of June, 1868.</code> | <code>Share Treaty of Greenville. The Treaty of Greenville was signed August 3, 1795, between the United States, represented by Gen. Anthony Wayne, and chiefs of the Indian tribes located in the Northwest Territory, including the Wyandots, Delawares, Shawnees, Ottawas, Miamis, and others.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 128,
"gather_across_devices": false,
"directions": [
"query_to_doc"
],
"partition_mode": "joint",
"hardness_mode": null,
"hardness_strength": 0.0
}
```
### Evaluation Dataset
#### msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
* Dataset: [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
* Size: 1,000 evaluation samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 10 characters</li><li>mean: 32.61 characters</li><li>max: 110 characters</li></ul> | <ul><li>min: 81 characters</li><li>mean: 344.28 characters</li><li>max: 908 characters</li></ul> | <ul><li>min: 97 characters</li><li>mean: 342.31 characters</li><li>max: 963 characters</li></ul> |
* Samples:
| query | positive | negative |
|:------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what county is holly springs nc in</code> | <code>Holly Springs, North Carolina. Holly Springs is a town in Wake County, North Carolina, United States. As of the 2010 census, the town population was 24,661, over 2½ times its population in 2000. Contents.</code> | <code>The Mt. Holly Springs Park & Resort. One of the numerous trolley routes that carried people around the county at the turn of the century was the Carlisle & Mt. Holly Railway Company. The âHolly Trolleyâ as it came to be known was put into service by Patricio Russo and made its first run on May 14, 1901.</code> |
| <code>how long does nyquil stay in your system</code> | <code>In order to understand exactly how long Nyquil lasts, it is absolutely vital to learn about the various ingredients in the drug. One of the ingredients found in Nyquil is Doxylamine, which is an antihistamine. This specific medication has a biological half-life or 6 to 12 hours. With this in mind, it is possible for the drug to remain in the system for a period of 12 to 24 hours. It should be known that the specifics will depend on a wide variety of different factors, including your age and metabolism.</code> | <code>I confirmed that NyQuil is about 10% alcohol, a higher content than most domestic beers. When I asked about the relatively high proof, I was told that the alcohol dilutes the active ingredients. The alcohol free version is there for customers with addiction issues.. also found that in that version there is twice the amount of DXM. When I asked if I could speak to a chemist or scientist, I was told they didn't have anyone who fit that description there. Itâs been eight years since I kicked NyQuil. I've been sober from alcohol for four years.</code> |
| <code>what are mineral water</code> | <code>1 Mineral water â water from a mineral spring that contains various minerals, such as salts and sulfur compounds. 2 It comes from a source tapped at one or more bore holes or spring, and originates from a geologically and physically protected underground water source. Mineral water â water from a mineral spring that contains various minerals, such as salts and sulfur compounds. 2 It comes from a source tapped at one or more bore holes or spring, and originates from a geologically and physically protected underground water source.</code> | <code>Minerals for Your Body. Drinking mineral water is beneficial to health and well-being. But it is not only the amount of water you drink that is important-what the water contains is even more essential.inerals for Your Body. Drinking mineral water is beneficial to health and well-being. But it is not only the amount of water you drink that is important-what the water contains is even more essential.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 128,
"gather_across_devices": false,
"directions": [
"query_to_doc"
],
"partition_mode": "joint",
"hardness_mode": null,
"hardness_strength": 0.0
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 2048
- `num_train_epochs`: 1
- `learning_rate`: 8e-05
- `warmup_steps`: 0.05
- `bf16`: True
- `eval_strategy`: steps
- `per_device_eval_batch_size`: 2048
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `per_device_train_batch_size`: 2048
- `num_train_epochs`: 1
- `max_steps`: -1
- `learning_rate`: 8e-05
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0.05
- `optim`: adamw_torch_fused
- `optim_args`: None
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `optim_target_modules`: None
- `gradient_accumulation_steps`: 1
- `average_tokens_across_devices`: True
- `max_grad_norm`: 1.0
- `label_smoothing_factor`: 0.0
- `bf16`: True
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `use_cache`: False
- `neftune_noise_alpha`: None
- `torch_empty_cache_steps`: None
- `auto_find_batch_size`: False
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `include_num_input_tokens_seen`: no
- `log_level`: passive
- `log_level_replica`: warning
- `disable_tqdm`: False
- `project`: huggingface
- `trackio_space_id`: trackio
- `eval_strategy`: steps
- `per_device_eval_batch_size`: 2048
- `prediction_loss_only`: True
- `eval_on_start`: False
- `eval_do_concat_batches`: True
- `eval_use_gather_object`: False
- `eval_accumulation_steps`: None
- `include_for_metrics`: []
- `batch_eval_metrics`: False
- `save_only_model`: False
- `save_on_each_node`: False
- `enable_jit_checkpoint`: False
- `push_to_hub`: False
- `hub_private_repo`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_always_push`: False
- `hub_revision`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `restore_callback_states_from_checkpoint`: False
- `full_determinism`: False
- `seed`: 42
- `data_seed`: None
- `use_cpu`: False
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `dataloader_prefetch_factor`: None
- `remove_unused_columns`: True
- `label_names`: None
- `train_sampling_strategy`: random
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `ddp_backend`: None
- `ddp_timeout`: 1800
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `deepspeed`: None
- `debug`: []
- `skip_memory_metrics`: True
- `do_predict`: False
- `resume_from_checkpoint`: None
- `warmup_ratio`: None
- `local_rank`: -1
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | msmarco-co-condenser-eval-triplet_cosine_accuracy |
|:------:|:----:|:-------------:|:---------------:|:-------------------------------------------------:|
| -1 | -1 | - | - | 0.5410 |
| 0.0136 | 2 | 8.2614 | - | - |
| 0.0272 | 4 | 8.2470 | - | - |
| 0.0408 | 6 | 8.1877 | - | - |
| 0.0544 | 8 | 8.0314 | - | - |
| 0.0680 | 10 | 7.7203 | - | - |
| 0.0816 | 12 | 7.2604 | - | - |
| 0.0952 | 14 | 6.8749 | - | - |
| 0.1020 | 15 | - | 5.7117 | 0.6220 |
| 0.1088 | 16 | 6.5251 | - | - |
| 0.1224 | 18 | 6.1747 | - | - |
| 0.1361 | 20 | 5.8759 | - | - |
| 0.1497 | 22 | 5.5909 | - | - |
| 0.1633 | 24 | 5.3883 | - | - |
| 0.1769 | 26 | 5.1466 | - | - |
| 0.1905 | 28 | 4.8767 | - | - |
| 0.2041 | 30 | 4.6119 | 3.8599 | 0.6920 |
| 0.2177 | 32 | 4.4513 | - | - |
| 0.2313 | 34 | 4.2229 | - | - |
| 0.2449 | 36 | 3.9295 | - | - |
| 0.2585 | 38 | 3.7703 | - | - |
| 0.2721 | 40 | 3.5242 | - | - |
| 0.2857 | 42 | 3.3985 | - | - |
| 0.2993 | 44 | 3.2776 | - | - |
| 0.3061 | 45 | - | 2.5264 | 0.6390 |
| 0.3129 | 46 | 3.1696 | - | - |
| 0.3265 | 48 | 2.9440 | - | - |
| 0.3401 | 50 | 2.9842 | - | - |
| 0.3537 | 52 | 2.8640 | - | - |
| 0.3673 | 54 | 2.7503 | - | - |
| 0.3810 | 56 | 2.7603 | - | - |
| 0.3946 | 58 | 2.6417 | - | - |
| 0.4082 | 60 | 2.4575 | 2.0850 | 0.6370 |
| 0.4218 | 62 | 2.4319 | - | - |
| 0.4354 | 64 | 2.3469 | - | - |
| 0.4490 | 66 | 2.3213 | - | - |
| 0.4626 | 68 | 2.2758 | - | - |
| 0.4762 | 70 | 2.2147 | - | - |
| 0.4898 | 72 | 2.3848 | - | - |
| 0.5034 | 74 | 2.2504 | - | - |
| 0.5102 | 75 | - | 1.9316 | 0.6370 |
| 0.5170 | 76 | 2.2943 | - | - |
| 0.5306 | 78 | 2.2707 | - | - |
| 0.5442 | 80 | 2.2240 | - | - |
| 0.5578 | 82 | 2.1809 | - | - |
| 0.5714 | 84 | 2.2143 | - | - |
| 0.5850 | 86 | 2.0512 | - | - |
| 0.5986 | 88 | 2.0743 | - | - |
| 0.6122 | 90 | 2.2043 | 1.7521 | 0.6330 |
| 0.6259 | 92 | 2.1268 | - | - |
| 0.6395 | 94 | 1.9546 | - | - |
| 0.6531 | 96 | 2.0395 | - | - |
| 0.6667 | 98 | 2.0138 | - | - |
| 0.6803 | 100 | 1.9161 | - | - |
| 0.6939 | 102 | 2.0675 | - | - |
| 0.7075 | 104 | 1.9894 | - | - |
| 0.7143 | 105 | - | 1.6923 | 0.6420 |
| 0.7211 | 106 | 2.0513 | - | - |
| 0.7347 | 108 | 1.8997 | - | - |
| 0.7483 | 110 | 2.0753 | - | - |
| 0.7619 | 112 | 1.9403 | - | - |
| 0.7755 | 114 | 1.9421 | - | - |
| 0.7891 | 116 | 1.9446 | - | - |
| 0.8027 | 118 | 1.9905 | - | - |
| 0.8163 | 120 | 1.8458 | 1.6407 | 0.6370 |
| 0.8299 | 122 | 1.9369 | - | - |
| 0.8435 | 124 | 1.8938 | - | - |
| 0.8571 | 126 | 1.8699 | - | - |
| 0.8707 | 128 | 2.0305 | - | - |
| 0.8844 | 130 | 1.8765 | - | - |
| 0.8980 | 132 | 1.9484 | - | - |
| 0.9116 | 134 | 1.8669 | - | - |
| 0.9184 | 135 | - | 1.6204 | 0.6390 |
| 0.9252 | 136 | 1.9958 | - | - |
| 0.9388 | 138 | 1.9659 | - | - |
| 0.9524 | 140 | 1.8720 | - | - |
| 0.9660 | 142 | 1.9456 | - | - |
| 0.9796 | 144 | 1.8776 | - | - |
| 0.9932 | 146 | 1.9861 | - | - |
| -1 | -1 | - | - | 0.6400 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.255 kWh
- **Carbon Emitted**: 0.068 kg of CO2
- **Hours Used**: 0.787 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 5.4.0.dev0
- Transformers: 5.3.0.dev0
- PyTorch: 2.10.0+cu128
- Accelerate: 1.13.0.dev0
- Datasets: 4.3.0
- Tokenizers: 0.22.2
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@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",
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@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}
}
```
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