Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
biencoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:483820
loss:MultipleNegativesSymmetricRankingLoss
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 | |
| - cosine_accuracy_threshold | |
| - cosine_f1 | |
| - cosine_f1_threshold | |
| - cosine_precision | |
| - cosine_recall | |
| - cosine_ap | |
| - cosine_mcc | |
| model-index: | |
| - name: Redis fine-tuned BiEncoder model for semantic caching on LangCache | |
| results: | |
| - task: | |
| type: binary-classification | |
| name: Binary Classification | |
| dataset: | |
| name: test | |
| type: test | |
| metrics: | |
| - type: cosine_accuracy | |
| value: 0.7276245142774221 | |
| name: Cosine Accuracy | |
| - type: cosine_accuracy_threshold | |
| value: 0.8017503619194031 | |
| name: Cosine Accuracy Threshold | |
| - type: cosine_f1 | |
| value: 0.723032161181329 | |
| name: Cosine F1 | |
| - type: cosine_f1_threshold | |
| value: 0.7345461845397949 | |
| name: Cosine F1 Threshold | |
| - type: cosine_precision | |
| value: 0.6233076217703221 | |
| name: Cosine Precision | |
| - type: cosine_recall | |
| value: 0.8607448789571694 | |
| name: Cosine Recall | |
| - type: cosine_ap | |
| value: 0.7251364855292874 | |
| name: Cosine Ap | |
| - type: cosine_mcc | |
| value: 0.4684913821533736 | |
| name: Cosine Mcc | |
| # 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([[0.9961, 0.9961, 0.1250], | |
| # [0.9961, 0.9961, 0.1162], | |
| # [0.1250, 0.1162, 1.0078]], 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 | |
| #### Binary Classification | |
| * Dataset: `test` | |
| * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | |
| | Metric | Value | | |
| |:--------------------------|:-----------| | |
| | cosine_accuracy | 0.7276 | | |
| | cosine_accuracy_threshold | 0.8018 | | |
| | cosine_f1 | 0.723 | | |
| | cosine_f1_threshold | 0.7345 | | |
| | cosine_precision | 0.6233 | | |
| | cosine_recall | 0.8607 | | |
| | **cosine_ap** | **0.7251** | | |
| | cosine_mcc | 0.4685 | | |
| <!-- | |
| ## 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`: 100 | |
| - `per_device_eval_batch_size`: 100 | |
| - `learning_rate`: 0.0001 | |
| - `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`: 100 | |
| - `per_device_eval_batch_size`: 100 | |
| - `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`: 0.98 | |
| - `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 | test_cosine_ap | | |
| |:----------:|:--------:|:-------------:|:---------------:|:--------------:| | |
| | -1 | -1 | - | - | 0.6476 | | |
| | 0.2067 | 1000 | 0.0165 | 0.1033 | 0.6705 | | |
| | 0.4133 | 2000 | 0.0067 | 0.0977 | 0.6597 | | |
| | 0.6200 | 3000 | 0.0061 | 0.0955 | 0.6670 | | |
| | **0.8266** | **4000** | **0.0063** | **0.0945** | **0.6678** | | |
| | 1.0333 | 5000 | 0.0059 | 0.0950 | 0.6786 | | |
| | 1.2399 | 6000 | 0.0054 | 0.0880 | 0.6779 | | |
| | 1.4466 | 7000 | 0.0054 | 0.0876 | 0.6791 | | |
| | 1.6532 | 8000 | 0.0054 | 0.0833 | 0.6652 | | |
| | 1.8599 | 9000 | 0.0051 | 0.0821 | 0.6760 | | |
| | 2.0665 | 10000 | 0.0048 | 0.0818 | 0.6767 | | |
| | 2.2732 | 11000 | 0.0044 | 0.0796 | 0.6732 | | |
| | 2.4799 | 12000 | 0.0048 | 0.0790 | 0.6717 | | |
| | 2.6865 | 13000 | 0.0043 | 0.0804 | 0.6748 | | |
| | 2.8932 | 14000 | 0.0048 | 0.0790 | 0.6745 | | |
| | 3.0998 | 15000 | 0.0033 | 0.0775 | 0.6693 | | |
| | 3.3065 | 16000 | 0.0044 | 0.0769 | 0.6767 | | |
| | 3.5131 | 17000 | 0.005 | 0.0770 | 0.6768 | | |
| | 3.7198 | 18000 | 0.0044 | 0.0760 | 0.6761 | | |
| | 3.9264 | 19000 | 0.0039 | 0.0741 | 0.6799 | | |
| | 4.1331 | 20000 | 0.0044 | 0.0750 | 0.6888 | | |
| | 4.3397 | 21000 | 0.0041 | 0.0751 | 0.7019 | | |
| | 4.5464 | 22000 | 0.0044 | 0.0707 | 0.7009 | | |
| | 4.7530 | 23000 | 0.0039 | 0.0726 | 0.7041 | | |
| | 4.9597 | 24000 | 0.0042 | 0.0712 | 0.6971 | | |
| | 5.1664 | 25000 | 0.0038 | 0.0718 | 0.6978 | | |
| | 5.3730 | 26000 | 0.004 | 0.0703 | 0.7035 | | |
| | 5.5797 | 27000 | 0.004 | 0.0706 | 0.6976 | | |
| | 5.7863 | 28000 | 0.0042 | 0.0699 | 0.6964 | | |
| | 5.9930 | 29000 | 0.0044 | 0.0699 | 0.6911 | | |
| | 6.1996 | 30000 | 0.0035 | 0.0702 | 0.6791 | | |
| | 6.4063 | 31000 | 0.0035 | 0.0690 | 0.6955 | | |
| | 6.6129 | 32000 | 0.0037 | 0.0693 | 0.6917 | | |
| | 6.8196 | 33000 | 0.0035 | 0.0691 | 0.6972 | | |
| | 7.0262 | 34000 | 0.004 | 0.0695 | 0.7083 | | |
| | 7.2329 | 35000 | 0.0037 | 0.0690 | 0.6994 | | |
| | 7.4396 | 36000 | 0.0036 | 0.0670 | 0.7060 | | |
| | 7.6462 | 37000 | 0.0042 | 0.0682 | 0.6963 | | |
| | 7.8529 | 38000 | 0.0037 | 0.0678 | 0.7049 | | |
| | 8.0595 | 39000 | 0.0039 | 0.0682 | 0.7014 | | |
| | 8.2662 | 40000 | 0.0039 | 0.0684 | 0.6969 | | |
| | 8.4728 | 41000 | 0.0041 | 0.0677 | 0.7007 | | |
| | 8.6795 | 42000 | 0.0038 | 0.0671 | 0.7126 | | |
| | 8.8861 | 43000 | 0.0035 | 0.0684 | 0.7150 | | |
| | 9.0928 | 44000 | 0.0035 | 0.0671 | 0.7043 | | |
| | 9.2994 | 45000 | 0.0038 | 0.0681 | 0.7021 | | |
| | 9.5061 | 46000 | 0.0038 | 0.0687 | 0.7129 | | |
| | 9.7128 | 47000 | 0.0038 | 0.0684 | 0.7215 | | |
| | 9.9194 | 48000 | 0.0039 | 0.0668 | 0.7179 | | |
| | 10.1261 | 49000 | 0.0031 | 0.0661 | 0.7129 | | |
| | 10.3327 | 50000 | 0.0033 | 0.0664 | 0.7119 | | |
| | 10.5394 | 51000 | 0.0034 | 0.0668 | 0.7162 | | |
| | 10.7460 | 52000 | 0.0038 | 0.0666 | 0.7181 | | |
| | 10.9527 | 53000 | 0.0034 | 0.0674 | 0.7046 | | |
| | 11.1593 | 54000 | 0.0034 | 0.0657 | 0.7100 | | |
| | 11.3660 | 55000 | 0.0035 | 0.0656 | 0.7163 | | |
| | 11.5726 | 56000 | 0.0034 | 0.0656 | 0.7003 | | |
| | 11.7793 | 57000 | 0.0036 | 0.0643 | 0.7009 | | |
| | 11.9859 | 58000 | 0.0038 | 0.0649 | 0.7166 | | |
| | 12.1926 | 59000 | 0.0039 | 0.0659 | 0.7168 | | |
| | 12.3993 | 60000 | 0.0039 | 0.0647 | 0.7080 | | |
| | 12.6059 | 61000 | 0.0032 | 0.0649 | 0.7114 | | |
| | 12.8126 | 62000 | 0.0034 | 0.0646 | 0.7165 | | |
| | 13.0192 | 63000 | 0.0034 | 0.0654 | 0.7197 | | |
| | 13.2259 | 64000 | 0.0035 | 0.0657 | 0.7179 | | |
| | 13.4325 | 65000 | 0.0031 | 0.0652 | 0.7107 | | |
| | 13.6392 | 66000 | 0.0032 | 0.0649 | 0.7089 | | |
| | 13.8458 | 67000 | 0.0034 | 0.0655 | 0.7089 | | |
| | 14.0525 | 68000 | 0.0031 | 0.0668 | 0.7163 | | |
| | 14.2591 | 69000 | 0.0035 | 0.0644 | 0.7213 | | |
| | 14.4658 | 70000 | 0.0035 | 0.0634 | 0.7057 | | |
| | 14.6725 | 71000 | 0.0035 | 0.0635 | 0.7049 | | |
| | 14.8791 | 72000 | 0.0033 | 0.0627 | 0.7094 | | |
| | 15.0858 | 73000 | 0.0037 | 0.0620 | 0.7140 | | |
| | 15.2924 | 74000 | 0.0035 | 0.0628 | 0.7237 | | |
| | 15.4991 | 75000 | 0.003 | 0.0625 | 0.7127 | | |
| | 15.7057 | 76000 | 0.0036 | 0.0635 | 0.7127 | | |
| | 15.9124 | 77000 | 0.0037 | 0.0621 | 0.7104 | | |
| | 16.1190 | 78000 | 0.0033 | 0.0624 | 0.7132 | | |
| | 16.3257 | 79000 | 0.0035 | 0.0632 | 0.7132 | | |
| | 16.5323 | 80000 | 0.003 | 0.0626 | 0.7193 | | |
| | 16.7390 | 81000 | 0.0033 | 0.0628 | 0.7179 | | |
| | 16.9456 | 82000 | 0.0036 | 0.0630 | 0.7210 | | |
| | 17.1523 | 83000 | 0.0033 | 0.0628 | 0.7222 | | |
| | 17.3590 | 84000 | 0.0034 | 0.0629 | 0.7226 | | |
| | 17.5656 | 85000 | 0.0029 | 0.0621 | 0.7207 | | |
| | 17.7723 | 86000 | 0.0032 | 0.0618 | 0.7182 | | |
| | 17.9789 | 87000 | 0.0034 | 0.0620 | 0.7177 | | |
| | 18.1856 | 88000 | 0.0034 | 0.0625 | 0.7148 | | |
| | 18.3922 | 89000 | 0.0032 | 0.0624 | 0.7131 | | |
| | 18.5989 | 90000 | 0.0032 | 0.0622 | 0.7126 | | |
| | 18.8055 | 91000 | 0.0031 | 0.0617 | 0.7185 | | |
| | 19.0122 | 92000 | 0.0032 | 0.0620 | 0.7231 | | |
| | 19.2188 | 93000 | 0.0028 | 0.0623 | 0.7202 | | |
| | 19.4255 | 94000 | 0.003 | 0.0625 | 0.7194 | | |
| | 19.6322 | 95000 | 0.003 | 0.0619 | 0.7139 | | |
| | 19.8388 | 96000 | 0.0031 | 0.0621 | 0.7151 | | |
| | 20.0455 | 97000 | 0.0031 | 0.0617 | 0.7188 | | |
| | 20.2521 | 98000 | 0.0031 | 0.0619 | 0.7161 | | |
| | 20.4588 | 99000 | 0.0027 | 0.0612 | 0.7164 | | |
| | 20.6654 | 100000 | 0.0033 | 0.0616 | 0.7173 | | |
| | 20.8721 | 101000 | 0.0033 | 0.0614 | 0.7182 | | |
| | 21.0787 | 102000 | 0.003 | 0.0611 | 0.7194 | | |
| | 21.2854 | 103000 | 0.0031 | 0.0614 | 0.7191 | | |
| | 21.4920 | 104000 | 0.0031 | 0.0615 | 0.7187 | | |
| | 21.6987 | 105000 | 0.0035 | 0.0609 | 0.7143 | | |
| | 21.9054 | 106000 | 0.0033 | 0.0614 | 0.7180 | | |
| | 22.1120 | 107000 | 0.0029 | 0.0608 | 0.7215 | | |
| | 22.3187 | 108000 | 0.0032 | 0.0609 | 0.7250 | | |
| | 22.5253 | 109000 | 0.0029 | 0.0611 | 0.7248 | | |
| | 22.7320 | 110000 | 0.003 | 0.0612 | 0.7224 | | |
| | 22.9386 | 111000 | 0.0029 | 0.0612 | 0.7180 | | |
| | 23.1453 | 112000 | 0.0032 | 0.0610 | 0.7169 | | |
| | 23.3519 | 113000 | 0.0032 | 0.0609 | 0.7174 | | |
| | 23.5586 | 114000 | 0.0028 | 0.0613 | 0.7204 | | |
| | 23.7652 | 115000 | 0.0033 | 0.0613 | 0.7222 | | |
| | 23.9719 | 116000 | 0.0033 | 0.0613 | 0.7240 | | |
| | 24.1785 | 117000 | 0.003 | 0.0610 | 0.7244 | | |
| | 24.3852 | 118000 | 0.0027 | 0.0613 | 0.7239 | | |
| | 24.5919 | 119000 | 0.0028 | 0.0615 | 0.7248 | | |
| | 24.7985 | 120000 | 0.003 | 0.0608 | 0.7259 | | |
| | 25.0052 | 121000 | 0.0033 | 0.0605 | 0.7270 | | |
| | 25.2118 | 122000 | 0.0035 | 0.0604 | 0.7240 | | |
| | 25.4185 | 123000 | 0.003 | 0.0607 | 0.7245 | | |
| | 25.6251 | 124000 | 0.003 | 0.0608 | 0.7238 | | |
| | 25.8318 | 125000 | 0.0032 | 0.0605 | 0.7208 | | |
| | 26.0384 | 126000 | 0.0029 | 0.0605 | 0.7208 | | |
| | 26.2451 | 127000 | 0.0034 | 0.0603 | 0.7212 | | |
| | 26.4517 | 128000 | 0.003 | 0.0605 | 0.7222 | | |
| | 26.6584 | 129000 | 0.003 | 0.0604 | 0.7236 | | |
| | 26.8651 | 130000 | 0.003 | 0.0608 | 0.7271 | | |
| | 27.0717 | 131000 | 0.0028 | 0.0608 | 0.7242 | | |
| | 27.2784 | 132000 | 0.0028 | 0.0612 | 0.7239 | | |
| | 27.4850 | 133000 | 0.0025 | 0.0609 | 0.7270 | | |
| | 27.6917 | 134000 | 0.0026 | 0.0607 | 0.7277 | | |
| | 27.8983 | 135000 | 0.003 | 0.0608 | 0.7263 | | |
| | 28.1050 | 136000 | 0.003 | 0.0609 | 0.7250 | | |
| | 28.3116 | 137000 | 0.0029 | 0.0607 | 0.7262 | | |
| | 28.5183 | 138000 | 0.0029 | 0.0609 | 0.7269 | | |
| | 28.7249 | 139000 | 0.0029 | 0.0607 | 0.7250 | | |
| | 28.9316 | 140000 | 0.0025 | 0.0608 | 0.7254 | | |
| | 29.1383 | 141000 | 0.0031 | 0.0609 | 0.7262 | | |
| | 29.3449 | 142000 | 0.0027 | 0.0606 | 0.7247 | | |
| | 29.5516 | 143000 | 0.003 | 0.0607 | 0.7244 | | |
| | 29.7582 | 144000 | 0.0028 | 0.0606 | 0.7240 | | |
| | 29.9649 | 145000 | 0.0028 | 0.0605 | 0.7228 | | |
| | 30.1715 | 146000 | 0.0032 | 0.0604 | 0.7251 | | |
| | 30.3782 | 147000 | 0.0033 | 0.0603 | 0.7240 | | |
| | 30.5848 | 148000 | 0.0029 | 0.0604 | 0.7242 | | |
| | 30.7915 | 149000 | 0.0032 | 0.0603 | 0.7241 | | |
| | 30.9981 | 150000 | 0.0028 | 0.0602 | 0.7246 | | |
| | 31.2048 | 151000 | 0.0029 | 0.0602 | 0.7261 | | |
| | 31.4114 | 152000 | 0.003 | 0.0602 | 0.7258 | | |
| | 31.6181 | 153000 | 0.0031 | 0.0603 | 0.7253 | | |
| | 31.8248 | 154000 | 0.003 | 0.0602 | 0.7250 | | |
| | 32.0314 | 155000 | 0.0033 | 0.0602 | 0.7248 | | |
| | 32.2381 | 156000 | 0.0031 | 0.0601 | 0.7248 | | |
| | 32.4447 | 157000 | 0.0027 | 0.0602 | 0.7240 | | |
| | 32.6514 | 158000 | 0.0026 | 0.0602 | 0.7243 | | |
| | 32.8580 | 159000 | 0.0028 | 0.0602 | 0.7249 | | |
| | 33.0647 | 160000 | 0.0033 | 0.0602 | 0.7251 | | |
| | 33.2713 | 161000 | 0.0031 | 0.0602 | 0.7252 | | |
| | 33.4780 | 162000 | 0.0027 | 0.0600 | 0.7247 | | |
| | 33.6846 | 163000 | 0.0031 | 0.0601 | 0.7247 | | |
| | 33.8913 | 164000 | 0.0032 | 0.0601 | 0.7251 | | |
| | 34.0980 | 165000 | 0.0026 | 0.0602 | 0.7252 | | |
| | 34.3046 | 166000 | 0.0034 | 0.0602 | 0.7252 | | |
| | 34.5113 | 167000 | 0.0028 | 0.0602 | 0.7250 | | |
| | 34.7179 | 168000 | 0.0029 | 0.0601 | 0.7249 | | |
| | 34.9246 | 169000 | 0.0028 | 0.0602 | 0.7253 | | |
| | 35.1312 | 170000 | 0.0026 | 0.0601 | 0.7249 | | |
| | 35.3379 | 171000 | 0.0027 | 0.0601 | 0.7247 | | |
| | 35.5445 | 172000 | 0.0031 | 0.0601 | 0.7245 | | |
| | 35.7512 | 173000 | 0.003 | 0.0600 | 0.7245 | | |
| | 35.9578 | 174000 | 0.003 | 0.0601 | 0.7250 | | |
| | 36.1645 | 175000 | 0.0027 | 0.0600 | 0.7246 | | |
| | 36.3712 | 176000 | 0.0028 | 0.0601 | 0.7248 | | |
| | 36.5778 | 177000 | 0.0027 | 0.0601 | 0.7250 | | |
| | 36.7845 | 178000 | 0.0028 | 0.0601 | 0.7252 | | |
| | 36.9911 | 179000 | 0.0029 | 0.0601 | 0.7252 | | |
| | 37.1978 | 180000 | 0.0029 | 0.0602 | 0.7251 | | |
| | 37.4044 | 181000 | 0.0025 | 0.0601 | 0.7250 | | |
| | 37.6111 | 182000 | 0.003 | 0.0601 | 0.7250 | | |
| | 37.8177 | 183000 | 0.0028 | 0.0601 | 0.7251 | | |
| | 38.0244 | 184000 | 0.0028 | 0.0601 | 0.7252 | | |
| | 38.2310 | 185000 | 0.0034 | 0.0600 | 0.7251 | | |
| | 38.4377 | 186000 | 0.0028 | 0.0601 | 0.7251 | | |
| | 38.6443 | 187000 | 0.0035 | 0.0601 | 0.7250 | | |
| | 38.8510 | 188000 | 0.003 | 0.0600 | 0.7250 | | |
| | 39.0577 | 189000 | 0.0028 | 0.0601 | 0.7252 | | |
| | 39.2643 | 190000 | 0.0027 | 0.0600 | 0.7250 | | |
| | 39.4710 | 191000 | 0.0026 | 0.0601 | 0.7250 | | |
| | 39.6776 | 192000 | 0.0028 | 0.0600 | 0.7251 | | |
| | 39.8843 | 193000 | 0.0027 | 0.0600 | 0.7251 | | |
| | 40.0909 | 194000 | 0.0031 | 0.0601 | 0.7252 | | |
| | 40.2976 | 195000 | 0.0031 | 0.0600 | 0.7252 | | |
| | 40.5042 | 196000 | 0.0029 | 0.0601 | 0.7251 | | |
| | 40.7109 | 197000 | 0.0032 | 0.0600 | 0.7251 | | |
| | 40.9175 | 198000 | 0.0028 | 0.0600 | 0.7251 | | |
| | 41.1242 | 199000 | 0.0029 | 0.0600 | 0.7252 | | |
| | 41.3309 | 200000 | 0.003 | 0.0600 | 0.7251 | | |
| * 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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