Add new SentenceTransformer model.

d5a352c verified about 2 years ago

41.3 kB

base_model: Snowflake/snowflake-arctic-embed-m
language:
  - en
library_name: sentence-transformers
license: apache-2.0
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:1K<n<10K
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: kim był Steve Yzerman?
    sentences:
      - Łazik marsjański Opportunity
      - w jakim kraju jest przyznawany Order Białego Lotosu?
      - do powstania jakich instytucji przyczynił się pierwszy biskup Makau?
  - source_sentence: gdzie rośnie bokkonia?
    sentences:
      - jak rozmnażają się Aeolosomatidae?
      - kto 1 stycznia 2011 został gubernatorem Nowego Jorku?
      - w której świątyni koronowany był król jerozolimski Baldwin I?
  - source_sentence: Godło Republiki Ałtaju
    sentences:
      - co przedstawia godło Republiki Ałtaju?
      - w którym kraju w noc sylwestrową je się oliebollen?
      - który z członków załogi Międzynarodowej Stacji Kosmicznej nie ma nóg?
  - source_sentence: co to jest meszne?
    sentences:
      - co to jest Mammoth Hot Springs?
      - jak przebiegała kariera sportowa Witolda Sikorskiego?
      - do uratowania ilu dzieł sztuki przyczynił się Borys Woźnicki?
  - source_sentence: Chłopiec z Nariokotome
    sentences:
      - ile wynosiła objętość mózgu chłopca z Nariokotome?
      - gdzie znajduje się czwarty polski cmentarz katyński?
      - w jakich miejscach stał warszawski pomnik Ignacego Jana Paderewskiego?
model-index:
  - name: snowflake-arctic-embed-m-klej-dyk
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.18509615384615385
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.4807692307692308
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.625
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7259615384615384
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.18509615384615385
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.16025641025641024
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.125
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07259615384615384
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.18509615384615385
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.4807692307692308
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.625
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.7259615384615384
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.44786216254546357
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.358972451159951
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3672210078826913
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.17548076923076922
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.47115384615384615
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6129807692307693
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7019230769230769
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.17548076923076922
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.15705128205128205
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.12259615384615384
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07019230769230768
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.17548076923076922
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.47115384615384615
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.6129807692307693
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.7019230769230769
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.43344535381311455
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3473920177045177
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3563798565478224
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.15625
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.4543269230769231
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5649038461538461
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6730769230769231
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.15625
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.15144230769230768
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.11298076923076923
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0673076923076923
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.15625
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.4543269230769231
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5649038461538461
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6730769230769231
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.4102597093872519
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.32613324175824177
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3350744652348361
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.16346153846153846
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.3918269230769231
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5072115384615384
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6057692307692307
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.16346153846153846
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.13060897435897434
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.10144230769230769
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06057692307692307
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.16346153846153846
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.3918269230769231
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5072115384615384
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6057692307692307
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3757626519143444
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.30273962148962136
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3116992239855167
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.14903846153846154
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.3389423076923077
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.4182692307692308
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.49278846153846156
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.14903846153846154
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.11298076923076923
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.08365384615384615
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.04927884615384615
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.14903846153846154
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.3389423076923077
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.4182692307692308
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.49278846153846156
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.31783226267644227
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.26212320665445676
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.27044860532149884
            name: Cosine Map@100

snowflake-arctic-embed-m-klej-dyk

This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-m. It maps sentences & paragraphs to a 768-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: Snowflake/snowflake-arctic-embed-m
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Chłopiec z Nariokotome',
    'ile wynosiła objętość mózgu chłopca z Nariokotome?',
    'gdzie znajduje się czwarty polski cmentarz katyński?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1851
cosine_accuracy@3	0.4808
cosine_accuracy@5	0.625
cosine_accuracy@10	0.726
cosine_precision@1	0.1851
cosine_precision@3	0.1603
cosine_precision@5	0.125
cosine_precision@10	0.0726
cosine_recall@1	0.1851
cosine_recall@3	0.4808
cosine_recall@5	0.625
cosine_recall@10	0.726
cosine_ndcg@10	0.4479
cosine_mrr@10	0.359
cosine_map@100	0.3672

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1755
cosine_accuracy@3	0.4712
cosine_accuracy@5	0.613
cosine_accuracy@10	0.7019
cosine_precision@1	0.1755
cosine_precision@3	0.1571
cosine_precision@5	0.1226
cosine_precision@10	0.0702
cosine_recall@1	0.1755
cosine_recall@3	0.4712
cosine_recall@5	0.613
cosine_recall@10	0.7019
cosine_ndcg@10	0.4334
cosine_mrr@10	0.3474
cosine_map@100	0.3564

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1562
cosine_accuracy@3	0.4543
cosine_accuracy@5	0.5649
cosine_accuracy@10	0.6731
cosine_precision@1	0.1562
cosine_precision@3	0.1514
cosine_precision@5	0.113
cosine_precision@10	0.0673
cosine_recall@1	0.1562
cosine_recall@3	0.4543
cosine_recall@5	0.5649
cosine_recall@10	0.6731
cosine_ndcg@10	0.4103
cosine_mrr@10	0.3261
cosine_map@100	0.3351

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1635
cosine_accuracy@3	0.3918
cosine_accuracy@5	0.5072
cosine_accuracy@10	0.6058
cosine_precision@1	0.1635
cosine_precision@3	0.1306
cosine_precision@5	0.1014
cosine_precision@10	0.0606
cosine_recall@1	0.1635
cosine_recall@3	0.3918
cosine_recall@5	0.5072
cosine_recall@10	0.6058
cosine_ndcg@10	0.3758
cosine_mrr@10	0.3027
cosine_map@100	0.3117

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.149
cosine_accuracy@3	0.3389
cosine_accuracy@5	0.4183
cosine_accuracy@10	0.4928
cosine_precision@1	0.149
cosine_precision@3	0.113
cosine_precision@5	0.0837
cosine_precision@10	0.0493
cosine_recall@1	0.149
cosine_recall@3	0.3389
cosine_recall@5	0.4183
cosine_recall@10	0.4928
cosine_ndcg@10	0.3178
cosine_mrr@10	0.2621
cosine_map@100	0.2704

Training Details

Training Dataset

Unnamed Dataset

Size: 3,738 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 6 tokens
mean: 94.61 tokens
max: 512 tokens

min: 10 tokens
mean: 30.71 tokens
max: 76 tokens

	positive	anchor
type	string	string
details	min: 6 tokens mean: 94.61 tokens max: 512 tokens	min: 10 tokens mean: 30.71 tokens max: 76 tokens

Samples:

positive	anchor
`Marsz Ochotników (chin.`	`kto jest kompozytorem chińskiego hymnu narodowego Marsz Ochotników?`
Wybrane przykłady: Święta Rodzina – Maryja z Dzieciątkiem na ręku, niekiedy obok niej stoi św. Józef Rodzina Marii – przedstawienie w którym pojawia się Święta Rodzina oraz postaci spokrewnione z Marią. Maria w połogu (Maria in puerperio) – leżąca na łożu Maria opiekuje się Dzieciątkiem Maria karmiąca (Maria lactans) – Maria karmiąca swą piersią Dzieciątko Orantka – kobieta modląca się z podniesionymi rękami (częsty motyw ikon wschodnich); Sacra Conversazione – Matka Boska tronująca z Dzieciątkiem, otoczona stojącymi postaciami świętych Pietà – opłakująca Jezusa, trzymając na kolanach jego ciało po śmierci na krzyżu; Hodegetria – ujęcie popiersia Maryi, trzymającej na rękach małego Jezusa, częsty motyw w ikonach Eleusa – formalnie podobne do przedstawienia Hodegetrii lecz Maryja policzkiem przytula się do policzka Jezusa Immaculata – Niepokalane Poczęcie Najświętszej Maryi Panny.	`kto zamiast Maryi trzyma nowonarodzonego Jezusa w scenie Bożego Narodzenia przedstawionej na poliptyku z Marią i Dzieciątkiem Jezus?`
`Pomnik Josepha von Eichendorffa w Brzeziu Pomnik Josepha von Eichendorffa – odtworzony w 2006 roku pomnik znanego niemieckiego poety epoki romantyzmu związanego z ziemią raciborską, Josepha von Eichendorffa.`	`po ilu latach odtworzono wysadzony w 1945 roku pomnik Josepha von Eichendorffa w Raciborzu-Brzeziu?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 5
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: True
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: True
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
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': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': 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
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.0684	1	9.3155	-	-	-	-	-
0.1368	2	9.1788	-	-	-	-	-
0.2051	3	8.8387	-	-	-	-	-
0.2735	4	8.2961	-	-	-	-	-
0.3419	5	8.0242	-	-	-	-	-
0.4103	6	7.2329	-	-	-	-	-
0.4786	7	5.4386	-	-	-	-	-
0.5470	8	6.1186	-	-	-	-	-
0.6154	9	4.9714	-	-	-	-	-
0.6838	10	5.1958	-	-	-	-	-
0.7521	11	5.1135	-	-	-	-	-
0.8205	12	4.6971	-	-	-	-	-
0.8889	13	4.5559	-	-	-	-	-
0.9573	14	3.9357	0.2842	0.3098	0.3191	0.2238	0.3209
1.0256	15	3.7916	-	-	-	-	-
1.0940	16	3.6393	-	-	-	-	-
1.1624	17	3.7733	-	-	-	-	-
1.2308	18	3.6974	-	-	-	-	-
1.2991	19	3.5964	-	-	-	-	-
1.3675	20	3.4118	-	-	-	-	-
1.4359	21	3.2022	-	-	-	-	-
1.5043	22	2.8133	-	-	-	-	-
1.5726	23	3.0871	-	-	-	-	-
1.6410	24	2.9559	-	-	-	-	-
1.7094	25	2.8192	-	-	-	-	-
1.7778	26	3.462	-	-	-	-	-
1.8462	27	3.1435	-	-	-	-	-
1.9145	28	2.8001	-	-	-	-	-
1.9829	29	2.5643	0.3134	0.3359	0.3563	0.2588	0.3671
2.0513	30	2.4295	-	-	-	-	-
2.1197	31	2.3892	-	-	-	-	-
2.1880	32	2.5228	-	-	-	-	-
2.2564	33	2.4906	-	-	-	-	-
2.3248	34	2.5358	-	-	-	-	-
2.3932	35	2.2806	-	-	-	-	-
2.4615	36	2.0083	-	-	-	-	-
2.5299	37	2.5088	-	-	-	-	-
2.5983	38	2.0628	-	-	-	-	-
2.6667	39	2.193	-	-	-	-	-
2.7350	40	2.4783	-	-	-	-	-
2.8034	41	2.382	-	-	-	-	-
2.8718	42	2.2017	-	-	-	-	-
2.9402	43	1.9739	0.3111	0.3392	0.3572	0.2657	0.3659
3.0085	44	2.0332	-	-	-	-	-
3.0769	45	1.9983	-	-	-	-	-
3.1453	46	1.8612	-	-	-	-	-
3.2137	47	1.9897	-	-	-	-	-
3.2821	48	2.2514	-	-	-	-	-
3.3504	49	2.0092	-	-	-	-	-
3.4188	50	1.7399	-	-	-	-	-
3.4872	51	1.5825	-	-	-	-	-
3.5556	52	2.1501	-	-	-	-	-
3.6239	53	1.4505	-	-	-	-	-
3.6923	54	1.8575	-	-	-	-	-
3.7607	55	2.3882	-	-	-	-	-
3.8291	56	2.1119	-	-	-	-	-
3.8974	57	1.8992	-	-	-	-	-
3.9658	58	1.8323	0.3117	0.3365	0.3558	0.2683	0.3670
4.0342	59	1.5938	-	-	-	-	-
4.1026	60	1.552	-	-	-	-	-
4.1709	61	1.907	-	-	-	-	-
4.2393	62	1.8304	-	-	-	-	-
4.3077	63	1.8775	-	-	-	-	-
4.3761	64	1.8654	-	-	-	-	-
4.4444	65	1.7944	-	-	-	-	-
4.5128	66	1.8335	-	-	-	-	-
4.5812	67	1.8823	-	-	-	-	-
4.6496	68	1.6479	-	-	-	-	-
4.7179	69	1.5771	-	-	-	-	-
4.7863	70	2.1911	0.3117	0.3351	0.3564	0.2704	0.3672

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.2
Sentence Transformers: 3.0.0
Transformers: 4.41.2
PyTorch: 2.3.1
Accelerate: 0.27.2
Datasets: 2.19.1
Tokenizers: 0.19.1

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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}