Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
biencoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:1451941
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
| language: | |
| - en | |
| license: apache-2.0 | |
| tags: | |
| - biencoder | |
| - sentence-transformers | |
| - text-classification | |
| - sentence-pair-classification | |
| - semantic-similarity | |
| - semantic-search | |
| - retrieval | |
| - reranking | |
| - generated_from_trainer | |
| - dataset_size:483820 | |
| - loss:MultipleNegativesSymmetricRankingLoss | |
| base_model: Alibaba-NLP/gte-modernbert-base | |
| widget: | |
| - source_sentence: 'See Precambrian time scale # Proposed Geologic timeline for another | |
| set of periods 4600 -- 541 MYA .' | |
| sentences: | |
| - In 2014 election , Biju Janata Dal candidate Tathagat Satapathy Bharatiya Janata | |
| party candidate Rudra Narayan Pany defeated with a margin of 1.37,340 votes . | |
| - In Scotland , the Strathclyde Partnership for Transport , formerly known as Strathclyde | |
| Passenger Transport Executive , comprises the former Strathclyde region , which | |
| includes the urban area around Glasgow . | |
| - 'See Precambrian Time Scale # Proposed Geological Timeline for another set of | |
| periods of 4600 -- 541 MYA .' | |
| - source_sentence: It is also 5 kilometers northeast of Tamaqua , 27 miles south of | |
| Allentown and 9 miles northwest of Hazleton . | |
| sentences: | |
| - In 1948 he moved to Massachusetts , and eventually settled in Vermont . | |
| - Suddenly I remembered that I was a New Zealander , I caught the first plane home | |
| and came back . | |
| - It is also 5 miles northeast of Tamaqua , 27 miles south of Allentown , and 9 | |
| miles northwest of Hazleton . | |
| - source_sentence: The party has a Member of Parliament , a member of the House of | |
| Lords , three members of the London Assembly and two Members of the European Parliament | |
| . | |
| sentences: | |
| - The party has one Member of Parliament , one member of the House of Lords , three | |
| Members of the London Assembly and two Members of the European Parliament . | |
| - Grapsid crabs dominate in Australia , Malaysia and Panama , while gastropods Cerithidea | |
| scalariformis and Melampus coeffeus are important seed predators in Florida mangroves | |
| . | |
| - Music Story is a music service website and international music data provider that | |
| curates , aggregates and analyses metadata for digital music services . | |
| - source_sentence: 'The play received two 1969 Tony Award nominations : Best Actress | |
| in a Play ( Michael Annals ) and Best Costume Design ( Charlotte Rae ) .' | |
| sentences: | |
| - Ravishanker is a fellow of the International Statistical Institute and an elected | |
| member of the American Statistical Association . | |
| - 'In 1969 , the play received two Tony - Award nominations : Best Actress in a | |
| Theatre Play ( Michael Annals ) and Best Costume Design ( Charlotte Rae ) .' | |
| - AMD and Nvidia both have proprietary methods of scaling , CrossFireX for AMD , | |
| and SLI for Nvidia . | |
| - source_sentence: He was a close friend of Ángel Cabrera and is a cousin of golfer | |
| Tony Croatto . | |
| sentences: | |
| - He was a close friend of Ángel Cabrera , and is a cousin of golfer Tony Croatto | |
| . | |
| - Eugenijus Bartulis ( born December 7 , 1949 in Kaunas ) is a Lithuanian Roman | |
| Catholic priest , and Bishop of Šiauliai . | |
| - UWIRE also distributes its members content to professional media outlets , including | |
| Yahoo , CNN and CBS News . | |
| datasets: | |
| - redis/langcache-sentencepairs-v1 | |
| pipeline_tag: sentence-similarity | |
| library_name: sentence-transformers | |
| metrics: | |
| - cosine_accuracy@1 | |
| - cosine_precision@1 | |
| - cosine_recall@1 | |
| - cosine_ndcg@10 | |
| - cosine_mrr@1 | |
| - cosine_map@100 | |
| model-index: | |
| - name: Redis fine-tuned BiEncoder model for semantic caching on LangCache | |
| results: | |
| - task: | |
| type: information-retrieval | |
| name: Information Retrieval | |
| dataset: | |
| name: train | |
| type: train | |
| metrics: | |
| - type: cosine_accuracy@1 | |
| value: 0.5978783286425633 | |
| name: Cosine Accuracy@1 | |
| - type: cosine_precision@1 | |
| value: 0.5978783286425633 | |
| name: Cosine Precision@1 | |
| - type: cosine_recall@1 | |
| value: 0.5765917883925028 | |
| name: Cosine Recall@1 | |
| - type: cosine_ndcg@10 | |
| value: 0.7905393533594786 | |
| name: Cosine Ndcg@10 | |
| - type: cosine_mrr@1 | |
| value: 0.5978783286425633 | |
| name: Cosine Mrr@1 | |
| - type: cosine_map@100 | |
| value: 0.7375956597574003 | |
| name: Cosine Map@100 | |
| # Redis fine-tuned BiEncoder model for semantic caching on LangCache | |
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for sentence pair similarity. | |
| ## Model Details | |
| ### Model Description | |
| - **Model Type:** Sentence Transformer | |
| - **Base model:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 --> | |
| - **Maximum Sequence Length:** 100 tokens | |
| - **Output Dimensionality:** 768 dimensions | |
| - **Similarity Function:** Cosine Similarity | |
| - **Training Dataset:** | |
| - [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) | |
| - **Language:** en | |
| - **License:** apache-2.0 | |
| ### Model Sources | |
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) | |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) | |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) | |
| ### Full Model Architecture | |
| ``` | |
| SentenceTransformer( | |
| (0): Transformer({'max_seq_length': 100, 'do_lower_case': False, 'architecture': 'ModernBertModel'}) | |
| (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}) | |
| ) | |
| ``` | |
| ## 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("redis/langcache-embed-v3") | |
| # Run inference | |
| sentences = [ | |
| 'He was a close friend of Ángel Cabrera and is a cousin of golfer Tony Croatto .', | |
| 'He was a close friend of Ángel Cabrera , and is a cousin of golfer Tony Croatto .', | |
| 'UWIRE also distributes its members content to professional media outlets , including Yahoo , CNN and CBS News .', | |
| ] | |
| embeddings = model.encode(sentences) | |
| print(embeddings.shape) | |
| # [3, 768] | |
| # Get the similarity scores for the embeddings | |
| similarities = model.similarity(embeddings, embeddings) | |
| print(similarities) | |
| # tensor([[1.0000, 0.9922, 0.0547], | |
| # [0.9922, 1.0000, 0.0449], | |
| # [0.0547, 0.0449, 1.0000]], dtype=torch.bfloat16) | |
| ``` | |
| <!-- | |
| ### 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 | |
| #### Information Retrieval | |
| * Dataset: `train` | |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | |
| | Metric | Value | | |
| |:-------------------|:-----------| | |
| | cosine_accuracy@1 | 0.5979 | | |
| | cosine_precision@1 | 0.5979 | | |
| | cosine_recall@1 | 0.5766 | | |
| | **cosine_ndcg@10** | **0.7905** | | |
| | cosine_mrr@1 | 0.5979 | | |
| | cosine_map@100 | 0.7376 | | |
| <!-- | |
| ## 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 | |
| #### LangCache Sentence Pairs (all) | |
| * Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) | |
| * Size: 26,850 training samples | |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> | |
| * Approximate statistics based on the first 1000 samples: | |
| | | sentence1 | sentence2 | label | | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------| | |
| | type | string | string | int | | |
| | details | <ul><li>min: 8 tokens</li><li>mean: 27.35 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> | | |
| * Samples: | |
| | sentence1 | sentence2 | label | | |
| |:----------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|:---------------| | |
| | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>1</code> | | |
| | <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code> | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code> | <code>1</code> | | |
| | <code>The 12F was officially homologated on August 21 , 1929 and exhibited at the Paris Salon in 1930 .</code> | <code>The 12F was officially homologated on 21 August 1929 and displayed at the 1930 Paris Salon .</code> | <code>1</code> | | |
| * Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters: | |
| ```json | |
| { | |
| "scale": 20.0, | |
| "similarity_fct": "cos_sim", | |
| "gather_across_devices": false | |
| } | |
| ``` | |
| ### Evaluation Dataset | |
| #### LangCache Sentence Pairs (all) | |
| * Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) | |
| * Size: 26,850 evaluation samples | |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> | |
| * Approximate statistics based on the first 1000 samples: | |
| | | sentence1 | sentence2 | label | | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------| | |
| | type | string | string | int | | |
| | details | <ul><li>min: 8 tokens</li><li>mean: 27.35 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> | | |
| * Samples: | |
| | sentence1 | sentence2 | label | | |
| |:----------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|:---------------| | |
| | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>1</code> | | |
| | <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code> | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code> | <code>1</code> | | |
| | <code>The 12F was officially homologated on August 21 , 1929 and exhibited at the Paris Salon in 1930 .</code> | <code>The 12F was officially homologated on 21 August 1929 and displayed at the 1930 Paris Salon .</code> | <code>1</code> | | |
| * Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters: | |
| ```json | |
| { | |
| "scale": 20.0, | |
| "similarity_fct": "cos_sim", | |
| "gather_across_devices": false | |
| } | |
| ``` | |
| ### Training Hyperparameters | |
| #### Non-Default Hyperparameters | |
| - `eval_strategy`: steps | |
| - `per_device_train_batch_size`: 256 | |
| - `per_device_eval_batch_size`: 256 | |
| - `learning_rate`: 0.0003 | |
| - `adam_beta2`: 0.98 | |
| - `adam_epsilon`: 1e-06 | |
| - `max_steps`: 200000 | |
| - `warmup_steps`: 1000 | |
| - `load_best_model_at_end`: True | |
| - `optim`: adamw_torch | |
| - `ddp_find_unused_parameters`: False | |
| - `push_to_hub`: True | |
| - `hub_model_id`: redis/langcache-embed-v3 | |
| - `batch_sampler`: no_duplicates | |
| #### All Hyperparameters | |
| <details><summary>Click to expand</summary> | |
| - `overwrite_output_dir`: False | |
| - `do_predict`: False | |
| - `eval_strategy`: steps | |
| - `prediction_loss_only`: True | |
| - `per_device_train_batch_size`: 256 | |
| - `per_device_eval_batch_size`: 256 | |
| - `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`: 0.0003 | |
| - `weight_decay`: 0.0 | |
| - `adam_beta1`: 0.9 | |
| - `adam_beta2`: 0.98 | |
| - `adam_epsilon`: 1e-06 | |
| - `max_grad_norm`: 1.0 | |
| - `num_train_epochs`: 3.0 | |
| - `max_steps`: 200000 | |
| - `lr_scheduler_type`: linear | |
| - `lr_scheduler_kwargs`: {} | |
| - `warmup_ratio`: 0.0 | |
| - `warmup_steps`: 1000 | |
| - `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`: False | |
| - `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`: 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} | |
| - `parallelism_config`: None | |
| - `deepspeed`: None | |
| - `label_smoothing_factor`: 0.0 | |
| - `optim`: adamw_torch | |
| - `optim_args`: None | |
| - `adafactor`: False | |
| - `group_by_length`: False | |
| - `length_column_name`: length | |
| - `ddp_find_unused_parameters`: False | |
| - `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`: True | |
| - `resume_from_checkpoint`: None | |
| - `hub_model_id`: redis/langcache-embed-v3 | |
| - `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`: False | |
| - `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`: False | |
| - `prompts`: None | |
| - `batch_sampler`: no_duplicates | |
| - `multi_dataset_batch_sampler`: proportional | |
| - `router_mapping`: {} | |
| - `learning_rate_mapping`: {} | |
| </details> | |
| ### Training Logs | |
| <details><summary>Click to expand</summary> | |
| | Epoch | Step | Training Loss | Validation Loss | train_cosine_ndcg@10 | | |
| |:-----------:|:---------:|:-------------:|:---------------:|:--------------------:| | |
| | -1 | -1 | - | - | 0.7522 | | |
| | 0.5291 | 1000 | 0.0231 | 0.1710 | 0.7518 | | |
| | 1.0582 | 2000 | 0.0147 | 0.1552 | 0.7593 | | |
| | 1.5873 | 3000 | 0.0126 | 0.1616 | 0.7603 | | |
| | 2.1164 | 4000 | 0.0113 | 0.1301 | 0.7644 | | |
| | 2.6455 | 5000 | 0.0119 | 0.1276 | 0.7659 | | |
| | 3.1746 | 6000 | 0.0099 | 0.1270 | 0.7648 | | |
| | 3.7037 | 7000 | 0.0101 | 0.1239 | 0.7676 | | |
| | 4.2328 | 8000 | 0.0093 | 0.1267 | 0.7709 | | |
| | 4.7619 | 9000 | 0.0092 | 0.1190 | 0.7711 | | |
| | 5.2910 | 10000 | 0.0088 | 0.1145 | 0.7735 | | |
| | 5.8201 | 11000 | 0.009 | 0.1172 | 0.7735 | | |
| | 6.3492 | 12000 | 0.0083 | 0.1144 | 0.7749 | | |
| | 6.8783 | 13000 | 0.0088 | 0.1140 | 0.7736 | | |
| | 7.4074 | 14000 | 0.0083 | 0.1134 | 0.7751 | | |
| | 7.9365 | 15000 | 0.0087 | 0.1108 | 0.7742 | | |
| | 8.4656 | 16000 | 0.0084 | 0.1119 | 0.7759 | | |
| | 8.9947 | 17000 | 0.0081 | 0.1125 | 0.7762 | | |
| | 9.5238 | 18000 | 0.0081 | 0.1134 | 0.7768 | | |
| | 10.0529 | 19000 | 0.008 | 0.1126 | 0.7766 | | |
| | 10.5820 | 20000 | 0.0079 | 0.1119 | 0.7755 | | |
| | 11.1111 | 21000 | 0.0078 | 0.1112 | 0.7781 | | |
| | 11.6402 | 22000 | 0.008 | 0.1113 | 0.7778 | | |
| | 12.1693 | 23000 | 0.0082 | 0.1066 | 0.7796 | | |
| | 12.6984 | 24000 | 0.0078 | 0.1098 | 0.7775 | | |
| | 13.2275 | 25000 | 0.0078 | 0.1089 | 0.7800 | | |
| | 13.7566 | 26000 | 0.0074 | 0.1091 | 0.7779 | | |
| | 14.2857 | 27000 | 0.0078 | 0.1061 | 0.7782 | | |
| | 14.8148 | 28000 | 0.0074 | 0.1073 | 0.7769 | | |
| | 15.3439 | 29000 | 0.0078 | 0.1022 | 0.7804 | | |
| | 15.8730 | 30000 | 0.0078 | 0.1035 | 0.7799 | | |
| | 16.4021 | 31000 | 0.0074 | 0.1046 | 0.7793 | | |
| | 16.9312 | 32000 | 0.0074 | 0.1043 | 0.7817 | | |
| | 17.4603 | 33000 | 0.0071 | 0.1056 | 0.7831 | | |
| | 17.9894 | 34000 | 0.0074 | 0.1022 | 0.7820 | | |
| | 18.5185 | 35000 | 0.0073 | 0.1035 | 0.7820 | | |
| | 19.0476 | 36000 | 0.0074 | 0.1020 | 0.7836 | | |
| | 19.5767 | 37000 | 0.0071 | 0.1036 | 0.7828 | | |
| | 20.1058 | 38000 | 0.007 | 0.1029 | 0.7845 | | |
| | 20.6349 | 39000 | 0.0071 | 0.1019 | 0.7835 | | |
| | 21.1640 | 40000 | 0.007 | 0.0991 | 0.7849 | | |
| | 21.6931 | 41000 | 0.0071 | 0.1013 | 0.7828 | | |
| | 22.2222 | 42000 | 0.0073 | 0.1033 | 0.7833 | | |
| | 22.7513 | 43000 | 0.0068 | 0.0996 | 0.7835 | | |
| | 23.2804 | 44000 | 0.007 | 0.0976 | 0.7850 | | |
| | 23.8095 | 45000 | 0.0069 | 0.0986 | 0.7840 | | |
| | 24.3386 | 46000 | 0.0068 | 0.0992 | 0.7856 | | |
| | 24.8677 | 47000 | 0.0068 | 0.0988 | 0.7838 | | |
| | 25.3968 | 48000 | 0.0068 | 0.0980 | 0.7857 | | |
| | 25.9259 | 49000 | 0.007 | 0.0976 | 0.7860 | | |
| | 26.4550 | 50000 | 0.0071 | 0.0994 | 0.7850 | | |
| | 26.9841 | 51000 | 0.0067 | 0.0984 | 0.7862 | | |
| | 27.5132 | 52000 | 0.0064 | 0.0992 | 0.7845 | | |
| | 28.0423 | 53000 | 0.0068 | 0.1021 | 0.7840 | | |
| | 28.5714 | 54000 | 0.0066 | 0.0974 | 0.7863 | | |
| | 29.1005 | 55000 | 0.0066 | 0.1001 | 0.7848 | | |
| | 29.6296 | 56000 | 0.0067 | 0.0997 | 0.7848 | | |
| | 30.1587 | 57000 | 0.0067 | 0.0965 | 0.7868 | | |
| | 30.6878 | 58000 | 0.0067 | 0.0968 | 0.7858 | | |
| | 31.2169 | 59000 | 0.0066 | 0.0973 | 0.7861 | | |
| | 31.7460 | 60000 | 0.0067 | 0.0972 | 0.7865 | | |
| | 32.2751 | 61000 | 0.0065 | 0.0991 | 0.7855 | | |
| | 32.8042 | 62000 | 0.0062 | 0.0960 | 0.7871 | | |
| | 33.3333 | 63000 | 0.0068 | 0.1006 | 0.7863 | | |
| | 33.8624 | 64000 | 0.0063 | 0.0980 | 0.7872 | | |
| | 34.3915 | 65000 | 0.0066 | 0.0957 | 0.7871 | | |
| | 34.9206 | 66000 | 0.0066 | 0.0971 | 0.7870 | | |
| | 35.4497 | 67000 | 0.0063 | 0.0982 | 0.7857 | | |
| | 35.9788 | 68000 | 0.0067 | 0.0944 | 0.7871 | | |
| | 36.5079 | 69000 | 0.0062 | 0.0961 | 0.7870 | | |
| | 37.0370 | 70000 | 0.0061 | 0.0924 | 0.7880 | | |
| | 37.5661 | 71000 | 0.0064 | 0.0928 | 0.7878 | | |
| | 38.0952 | 72000 | 0.0065 | 0.0934 | 0.7888 | | |
| | 38.6243 | 73000 | 0.0069 | 0.0948 | 0.7873 | | |
| | **39.1534** | **74000** | **0.0064** | **0.0922** | **0.7885** | | |
| | 39.6825 | 75000 | 0.0064 | 0.0937 | 0.7888 | | |
| | 40.2116 | 76000 | 0.0059 | 0.0941 | 0.7882 | | |
| | 40.7407 | 77000 | 0.0067 | 0.0934 | 0.7900 | | |
| | 41.2698 | 78000 | 0.0064 | 0.0926 | 0.7888 | | |
| | 41.7989 | 79000 | 0.006 | 0.0948 | 0.7880 | | |
| | 42.3280 | 80000 | 0.006 | 0.0953 | 0.7876 | | |
| | 42.8571 | 81000 | 0.0058 | 0.0955 | 0.7887 | | |
| | 43.3862 | 82000 | 0.0065 | 0.0945 | 0.7875 | | |
| | 43.9153 | 83000 | 0.0063 | 0.0928 | 0.7888 | | |
| | 44.4444 | 84000 | 0.0065 | 0.0959 | 0.7883 | | |
| | 44.9735 | 85000 | 0.0063 | 0.0956 | 0.7876 | | |
| | 45.5026 | 86000 | 0.006 | 0.0946 | 0.7893 | | |
| | 46.0317 | 87000 | 0.0062 | 0.0954 | 0.7908 | | |
| | 46.5608 | 88000 | 0.0061 | 0.0960 | 0.7896 | | |
| | 47.0899 | 89000 | 0.006 | 0.0953 | 0.7893 | | |
| | 47.6190 | 90000 | 0.0058 | 0.0941 | 0.7899 | | |
| | 48.1481 | 91000 | 0.0059 | 0.0950 | 0.7892 | | |
| | 48.6772 | 92000 | 0.0066 | 0.0948 | 0.7890 | | |
| | 49.2063 | 93000 | 0.0058 | 0.0947 | 0.7886 | | |
| | 49.7354 | 94000 | 0.006 | 0.0952 | 0.7891 | | |
| | 50.2646 | 95000 | 0.0058 | 0.0948 | 0.7885 | | |
| | 50.7937 | 96000 | 0.0058 | 0.0945 | 0.7894 | | |
| | 51.3228 | 97000 | 0.0059 | 0.0936 | 0.7901 | | |
| | 51.8519 | 98000 | 0.0059 | 0.0950 | 0.7900 | | |
| | 52.3810 | 99000 | 0.0058 | 0.0954 | 0.7893 | | |
| | 52.9101 | 100000 | 0.0058 | 0.0946 | 0.7900 | | |
| | 53.4392 | 101000 | 0.0056 | 0.0943 | 0.7900 | | |
| | 53.9683 | 102000 | 0.006 | 0.0950 | 0.7895 | | |
| | 54.4974 | 103000 | 0.0059 | 0.0937 | 0.7899 | | |
| | 55.0265 | 104000 | 0.0061 | 0.0941 | 0.7897 | | |
| | 55.5556 | 105000 | 0.0059 | 0.0941 | 0.7903 | | |
| | 56.0847 | 106000 | 0.0057 | 0.0924 | 0.7904 | | |
| | 56.6138 | 107000 | 0.006 | 0.0933 | 0.7901 | | |
| | 57.1429 | 108000 | 0.0059 | 0.0948 | 0.7888 | | |
| | 57.6720 | 109000 | 0.0061 | 0.0938 | 0.7899 | | |
| | 58.2011 | 110000 | 0.0058 | 0.0942 | 0.7904 | | |
| | 58.7302 | 111000 | 0.0056 | 0.0943 | 0.7913 | | |
| | 59.2593 | 112000 | 0.0056 | 0.0949 | 0.7915 | | |
| | 59.7884 | 113000 | 0.0058 | 0.0947 | 0.7907 | | |
| | 60.3175 | 114000 | 0.0058 | 0.0939 | 0.7910 | | |
| | 60.8466 | 115000 | 0.0058 | 0.0942 | 0.7906 | | |
| | 61.3757 | 116000 | 0.0055 | 0.0933 | 0.7910 | | |
| | 61.9048 | 117000 | 0.0055 | 0.0936 | 0.7913 | | |
| | 62.4339 | 118000 | 0.0059 | 0.0937 | 0.7904 | | |
| | 62.9630 | 119000 | 0.0057 | 0.0943 | 0.7908 | | |
| | 63.4921 | 120000 | 0.0056 | 0.0934 | 0.7912 | | |
| | 64.0212 | 121000 | 0.0058 | 0.0936 | 0.7909 | | |
| | 64.5503 | 122000 | 0.0055 | 0.0942 | 0.7896 | | |
| | 65.0794 | 123000 | 0.0058 | 0.0939 | 0.7901 | | |
| | 65.6085 | 124000 | 0.0057 | 0.0936 | 0.7907 | | |
| | 66.1376 | 125000 | 0.0054 | 0.0951 | 0.7901 | | |
| | 66.6667 | 126000 | 0.0055 | 0.0942 | 0.7912 | | |
| | 67.1958 | 127000 | 0.0057 | 0.0943 | 0.7914 | | |
| | 67.7249 | 128000 | 0.0057 | 0.0937 | 0.7910 | | |
| | 68.2540 | 129000 | 0.0057 | 0.0933 | 0.7918 | | |
| | 68.7831 | 130000 | 0.0055 | 0.0935 | 0.7913 | | |
| | 69.3122 | 131000 | 0.0053 | 0.0935 | 0.7908 | | |
| | 69.8413 | 132000 | 0.0057 | 0.0937 | 0.7905 | | |
| | 70.3704 | 133000 | 0.0055 | 0.0940 | 0.7912 | | |
| | 70.8995 | 134000 | 0.0052 | 0.0937 | 0.7913 | | |
| | 71.4286 | 135000 | 0.005 | 0.0940 | 0.7917 | | |
| | 71.9577 | 136000 | 0.0053 | 0.0933 | 0.7914 | | |
| | 72.4868 | 137000 | 0.0056 | 0.0940 | 0.7915 | | |
| | 73.0159 | 138000 | 0.0054 | 0.0937 | 0.7909 | | |
| | 73.5450 | 139000 | 0.0051 | 0.0940 | 0.7909 | | |
| | 74.0741 | 140000 | 0.0058 | 0.0938 | 0.7911 | | |
| | 74.6032 | 141000 | 0.0056 | 0.0938 | 0.7912 | | |
| | 75.1323 | 142000 | 0.0052 | 0.0931 | 0.7908 | | |
| | 75.6614 | 143000 | 0.0052 | 0.0937 | 0.7905 | | |
| | 76.1905 | 144000 | 0.0054 | 0.0940 | 0.7905 | | |
| | 76.7196 | 145000 | 0.0055 | 0.0940 | 0.7907 | | |
| | 77.2487 | 146000 | 0.0053 | 0.0941 | 0.7909 | | |
| | 77.7778 | 147000 | 0.0057 | 0.0944 | 0.7907 | | |
| | 78.3069 | 148000 | 0.0054 | 0.0947 | 0.7909 | | |
| | 78.8360 | 149000 | 0.0054 | 0.0949 | 0.7907 | | |
| | 79.3651 | 150000 | 0.0055 | 0.0948 | 0.7907 | | |
| | 79.8942 | 151000 | 0.0058 | 0.0950 | 0.7907 | | |
| | 80.4233 | 152000 | 0.0054 | 0.0946 | 0.7907 | | |
| | 80.9524 | 153000 | 0.0053 | 0.0949 | 0.7909 | | |
| | 81.4815 | 154000 | 0.0055 | 0.0947 | 0.7908 | | |
| | 82.0106 | 155000 | 0.0053 | 0.0946 | 0.7906 | | |
| | 82.5397 | 156000 | 0.0053 | 0.0949 | 0.7906 | | |
| | 83.0688 | 157000 | 0.0051 | 0.0948 | 0.7912 | | |
| | 83.5979 | 158000 | 0.0052 | 0.0954 | 0.7906 | | |
| | 84.1270 | 159000 | 0.0054 | 0.0953 | 0.7908 | | |
| | 84.6561 | 160000 | 0.005 | 0.0951 | 0.7911 | | |
| | 85.1852 | 161000 | 0.0054 | 0.0953 | 0.7910 | | |
| | 85.7143 | 162000 | 0.0056 | 0.0957 | 0.7907 | | |
| | 86.2434 | 163000 | 0.0054 | 0.0953 | 0.7909 | | |
| | 86.7725 | 164000 | 0.0051 | 0.0955 | 0.7912 | | |
| | 87.3016 | 165000 | 0.0055 | 0.0956 | 0.7911 | | |
| | 87.8307 | 166000 | 0.0056 | 0.0954 | 0.7909 | | |
| | 88.3598 | 167000 | 0.0052 | 0.0955 | 0.7911 | | |
| | 88.8889 | 168000 | 0.0052 | 0.0953 | 0.7910 | | |
| | 89.4180 | 169000 | 0.0052 | 0.0952 | 0.7906 | | |
| | 89.9471 | 170000 | 0.0053 | 0.0952 | 0.7908 | | |
| | 90.4762 | 171000 | 0.0052 | 0.0954 | 0.7908 | | |
| | 91.0053 | 172000 | 0.0054 | 0.0954 | 0.7907 | | |
| | 91.5344 | 173000 | 0.0052 | 0.0951 | 0.7909 | | |
| | 92.0635 | 174000 | 0.0053 | 0.0951 | 0.7907 | | |
| | 92.5926 | 175000 | 0.0051 | 0.0950 | 0.7906 | | |
| | 93.1217 | 176000 | 0.0054 | 0.0953 | 0.7907 | | |
| | 93.6508 | 177000 | 0.0052 | 0.0953 | 0.7907 | | |
| | 94.1799 | 178000 | 0.0051 | 0.0951 | 0.7908 | | |
| | 94.7090 | 179000 | 0.0052 | 0.0952 | 0.7906 | | |
| | 95.2381 | 180000 | 0.0053 | 0.0953 | 0.7909 | | |
| | 95.7672 | 181000 | 0.0052 | 0.0953 | 0.7908 | | |
| | 96.2963 | 182000 | 0.0051 | 0.0952 | 0.7906 | | |
| | 96.8254 | 183000 | 0.0053 | 0.0953 | 0.7907 | | |
| | 97.3545 | 184000 | 0.0051 | 0.0953 | 0.7907 | | |
| | 97.8836 | 185000 | 0.0049 | 0.0953 | 0.7906 | | |
| | 98.4127 | 186000 | 0.0051 | 0.0953 | 0.7907 | | |
| | 98.9418 | 187000 | 0.0051 | 0.0954 | 0.7906 | | |
| | 99.4709 | 188000 | 0.0053 | 0.0954 | 0.7906 | | |
| | 100.0 | 189000 | 0.0051 | 0.0954 | 0.7904 | | |
| | 100.5291 | 190000 | 0.0054 | 0.0953 | 0.7907 | | |
| | 101.0582 | 191000 | 0.0052 | 0.0954 | 0.7905 | | |
| | 101.5873 | 192000 | 0.0051 | 0.0954 | 0.7907 | | |
| | 102.1164 | 193000 | 0.0052 | 0.0953 | 0.7907 | | |
| | 102.6455 | 194000 | 0.0051 | 0.0955 | 0.7908 | | |
| | 103.1746 | 195000 | 0.0054 | 0.0954 | 0.7906 | | |
| | 103.7037 | 196000 | 0.0052 | 0.0954 | 0.7905 | | |
| | 104.2328 | 197000 | 0.0053 | 0.0954 | 0.7906 | | |
| | 104.7619 | 198000 | 0.0052 | 0.0954 | 0.7907 | | |
| | 105.2910 | 199000 | 0.0055 | 0.0954 | 0.7904 | | |
| | 105.8201 | 200000 | 0.0054 | 0.0955 | 0.7905 | | |
| * The bold row denotes the saved checkpoint. | |
| </details> | |
| ### Framework Versions | |
| - Python: 3.12.3 | |
| - Sentence Transformers: 5.1.0 | |
| - Transformers: 4.56.0 | |
| - PyTorch: 2.8.0+cu128 | |
| - Accelerate: 1.10.1 | |
| - Datasets: 4.0.0 | |
| - Tokenizers: 0.22.0 | |
| ## 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", | |
| } | |
| ``` | |
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