--- base_model: intfloat/multilingual-e5-small datasets: [] language: [] library_name: sentence-transformers metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - max_accuracy - max_accuracy_threshold - max_f1 - max_f1_threshold - max_precision - max_recall - max_ap pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:971 - loss:OnlineContrastiveLoss widget: - source_sentence: Steps to bake a pie sentences: - How to bake a pie? - What are the ingredients of a pizza? - How to create a business plan? - source_sentence: What are the benefits of yoga? sentences: - If I combine the yellow and blue colors, what color will I get? - Can you help me understand this contract? - What are the benefits of meditation? - source_sentence: Capital city of Canada sentences: - What time does the movie start? - Who is the President of the United States? - What is the capital of Canada? - source_sentence: Tell me about Shopify sentences: - Who discovered penicillin? - Share info about Shopify - Who invented the telephone? - source_sentence: What is the melting point of ice at sea level? sentences: - What is the boiling point of water at sea level? - Can you recommend a good restaurant nearby? - Tell me a joke model-index: - name: SentenceTransformer based on intfloat/multilingual-e5-small results: - task: type: binary-classification name: Binary Classification dataset: name: pair class dev type: pair-class-dev metrics: - type: cosine_accuracy value: 0.6337448559670782 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.9370981454849243 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.6735395189003436 name: Cosine F1 - type: cosine_f1_threshold value: 0.9088578224182129 name: Cosine F1 Threshold - type: cosine_precision value: 0.5355191256830601 name: Cosine Precision - type: cosine_recall value: 0.9074074074074074 name: Cosine Recall - type: cosine_ap value: 0.6318945658459245 name: Cosine Ap - type: dot_accuracy value: 0.6337448559670782 name: Dot Accuracy - type: dot_accuracy_threshold value: 0.9370982050895691 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6735395189003436 name: Dot F1 - type: dot_f1_threshold value: 0.9088578224182129 name: Dot F1 Threshold - type: dot_precision value: 0.5355191256830601 name: Dot Precision - type: dot_recall value: 0.9074074074074074 name: Dot Recall - type: dot_ap value: 0.6318945658459245 name: Dot Ap - type: manhattan_accuracy value: 0.6378600823045267 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 5.581961631774902 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.6712802768166088 name: Manhattan F1 - type: manhattan_f1_threshold value: 6.53279972076416 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.5359116022099447 name: Manhattan Precision - type: manhattan_recall value: 0.8981481481481481 name: Manhattan Recall - type: manhattan_ap value: 0.642597262545426 name: Manhattan Ap - type: euclidean_accuracy value: 0.6337448559670782 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 0.3546881079673767 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.6735395189003436 name: Euclidean F1 - type: euclidean_f1_threshold value: 0.42694616317749023 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.5355191256830601 name: Euclidean Precision - type: euclidean_recall value: 0.9074074074074074 name: Euclidean Recall - type: euclidean_ap value: 0.6318945658459245 name: Euclidean Ap - type: max_accuracy value: 0.6378600823045267 name: Max Accuracy - type: max_accuracy_threshold value: 5.581961631774902 name: Max Accuracy Threshold - type: max_f1 value: 0.6735395189003436 name: Max F1 - type: max_f1_threshold value: 6.53279972076416 name: Max F1 Threshold - type: max_precision value: 0.5359116022099447 name: Max Precision - type: max_recall value: 0.9074074074074074 name: Max Recall - type: max_ap value: 0.642597262545426 name: Max Ap - type: cosine_accuracy value: 0.9423868312757202 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7851011753082275 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9363636363636363 name: Cosine F1 - type: cosine_f1_threshold value: 0.7851011753082275 name: Cosine F1 Threshold - type: cosine_precision value: 0.9196428571428571 name: Cosine Precision - type: cosine_recall value: 0.9537037037037037 name: Cosine Recall - type: cosine_ap value: 0.9629460493565268 name: Cosine Ap - type: dot_accuracy value: 0.9423868312757202 name: Dot Accuracy - type: dot_accuracy_threshold value: 0.7851011753082275 name: Dot Accuracy Threshold - type: dot_f1 value: 0.9363636363636363 name: Dot F1 - type: dot_f1_threshold value: 0.7851011753082275 name: Dot F1 Threshold - type: dot_precision value: 0.9196428571428571 name: Dot Precision - type: dot_recall value: 0.9537037037037037 name: Dot Recall - type: dot_ap value: 0.9629460493565268 name: Dot Ap - type: manhattan_accuracy value: 0.9382716049382716 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 10.554386138916016 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.9333333333333333 name: Manhattan F1 - type: manhattan_f1_threshold value: 10.554386138916016 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.8974358974358975 name: Manhattan Precision - type: manhattan_recall value: 0.9722222222222222 name: Manhattan Recall - type: manhattan_ap value: 0.9614448856056382 name: Manhattan Ap - type: euclidean_accuracy value: 0.9423868312757202 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 0.6555726528167725 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.9363636363636363 name: Euclidean F1 - type: euclidean_f1_threshold value: 0.6555726528167725 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.9196428571428571 name: Euclidean Precision - type: euclidean_recall value: 0.9537037037037037 name: Euclidean Recall - type: euclidean_ap value: 0.9629460493565268 name: Euclidean Ap - type: max_accuracy value: 0.9423868312757202 name: Max Accuracy - type: max_accuracy_threshold value: 10.554386138916016 name: Max Accuracy Threshold - type: max_f1 value: 0.9363636363636363 name: Max F1 - type: max_f1_threshold value: 10.554386138916016 name: Max F1 Threshold - type: max_precision value: 0.9196428571428571 name: Max Precision - type: max_recall value: 0.9722222222222222 name: Max Recall - type: max_ap value: 0.9629460493565268 name: Max Ap - task: type: binary-classification name: Binary Classification dataset: name: pair class test type: pair-class-test metrics: - type: cosine_accuracy value: 0.9423868312757202 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7851011753082275 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9363636363636363 name: Cosine F1 - type: cosine_f1_threshold value: 0.7851011753082275 name: Cosine F1 Threshold - type: cosine_precision value: 0.9196428571428571 name: Cosine Precision - type: cosine_recall value: 0.9537037037037037 name: Cosine Recall - type: cosine_ap value: 0.9629460493565268 name: Cosine Ap - type: dot_accuracy value: 0.9423868312757202 name: Dot Accuracy - type: dot_accuracy_threshold value: 0.7851011753082275 name: Dot Accuracy Threshold - type: dot_f1 value: 0.9363636363636363 name: Dot F1 - type: dot_f1_threshold value: 0.7851011753082275 name: Dot F1 Threshold - type: dot_precision value: 0.9196428571428571 name: Dot Precision - type: dot_recall value: 0.9537037037037037 name: Dot Recall - type: dot_ap value: 0.9629460493565268 name: Dot Ap - type: manhattan_accuracy value: 0.9382716049382716 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 10.554386138916016 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.9333333333333333 name: Manhattan F1 - type: manhattan_f1_threshold value: 10.554386138916016 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.8974358974358975 name: Manhattan Precision - type: manhattan_recall value: 0.9722222222222222 name: Manhattan Recall - type: manhattan_ap value: 0.9614448856056382 name: Manhattan Ap - type: euclidean_accuracy value: 0.9423868312757202 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 0.6555726528167725 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.9363636363636363 name: Euclidean F1 - type: euclidean_f1_threshold value: 0.6555726528167725 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.9196428571428571 name: Euclidean Precision - type: euclidean_recall value: 0.9537037037037037 name: Euclidean Recall - type: euclidean_ap value: 0.9629460493565268 name: Euclidean Ap - type: max_accuracy value: 0.9423868312757202 name: Max Accuracy - type: max_accuracy_threshold value: 10.554386138916016 name: Max Accuracy Threshold - type: max_f1 value: 0.9363636363636363 name: Max F1 - type: max_f1_threshold value: 10.554386138916016 name: Max F1 Threshold - type: max_precision value: 0.9196428571428571 name: Max Precision - type: max_recall value: 0.9722222222222222 name: Max Recall - type: max_ap value: 0.9629460493565268 name: Max Ap --- # SentenceTransformer based on intfloat/multilingual-e5-small This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). 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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 tokens - **Similarity Function:** Cosine Similarity ### 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': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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: ```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("srikarvar/multilingual-e5-small-pairclass-3") # Run inference sentences = [ 'What is the melting point of ice at sea level?', 'What is the boiling point of water at sea level?', 'Can you recommend a good restaurant nearby?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Binary Classification * Dataset: `pair-class-dev` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.6337 | | cosine_accuracy_threshold | 0.9371 | | cosine_f1 | 0.6735 | | cosine_f1_threshold | 0.9089 | | cosine_precision | 0.5355 | | cosine_recall | 0.9074 | | cosine_ap | 0.6319 | | dot_accuracy | 0.6337 | | dot_accuracy_threshold | 0.9371 | | dot_f1 | 0.6735 | | dot_f1_threshold | 0.9089 | | dot_precision | 0.5355 | | dot_recall | 0.9074 | | dot_ap | 0.6319 | | manhattan_accuracy | 0.6379 | | manhattan_accuracy_threshold | 5.582 | | manhattan_f1 | 0.6713 | | manhattan_f1_threshold | 6.5328 | | manhattan_precision | 0.5359 | | manhattan_recall | 0.8981 | | manhattan_ap | 0.6426 | | euclidean_accuracy | 0.6337 | | euclidean_accuracy_threshold | 0.3547 | | euclidean_f1 | 0.6735 | | euclidean_f1_threshold | 0.4269 | | euclidean_precision | 0.5355 | | euclidean_recall | 0.9074 | | euclidean_ap | 0.6319 | | max_accuracy | 0.6379 | | max_accuracy_threshold | 5.582 | | max_f1 | 0.6735 | | max_f1_threshold | 6.5328 | | max_precision | 0.5359 | | max_recall | 0.9074 | | **max_ap** | **0.6426** | #### Binary Classification * Dataset: `pair-class-dev` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.9424 | | cosine_accuracy_threshold | 0.7851 | | cosine_f1 | 0.9364 | | cosine_f1_threshold | 0.7851 | | cosine_precision | 0.9196 | | cosine_recall | 0.9537 | | cosine_ap | 0.9629 | | dot_accuracy | 0.9424 | | dot_accuracy_threshold | 0.7851 | | dot_f1 | 0.9364 | | dot_f1_threshold | 0.7851 | | dot_precision | 0.9196 | | dot_recall | 0.9537 | | dot_ap | 0.9629 | | manhattan_accuracy | 0.9383 | | manhattan_accuracy_threshold | 10.5544 | | manhattan_f1 | 0.9333 | | manhattan_f1_threshold | 10.5544 | | manhattan_precision | 0.8974 | | manhattan_recall | 0.9722 | | manhattan_ap | 0.9614 | | euclidean_accuracy | 0.9424 | | euclidean_accuracy_threshold | 0.6556 | | euclidean_f1 | 0.9364 | | euclidean_f1_threshold | 0.6556 | | euclidean_precision | 0.9196 | | euclidean_recall | 0.9537 | | euclidean_ap | 0.9629 | | max_accuracy | 0.9424 | | max_accuracy_threshold | 10.5544 | | max_f1 | 0.9364 | | max_f1_threshold | 10.5544 | | max_precision | 0.9196 | | max_recall | 0.9722 | | **max_ap** | **0.9629** | #### Binary Classification * Dataset: `pair-class-test` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.9424 | | cosine_accuracy_threshold | 0.7851 | | cosine_f1 | 0.9364 | | cosine_f1_threshold | 0.7851 | | cosine_precision | 0.9196 | | cosine_recall | 0.9537 | | cosine_ap | 0.9629 | | dot_accuracy | 0.9424 | | dot_accuracy_threshold | 0.7851 | | dot_f1 | 0.9364 | | dot_f1_threshold | 0.7851 | | dot_precision | 0.9196 | | dot_recall | 0.9537 | | dot_ap | 0.9629 | | manhattan_accuracy | 0.9383 | | manhattan_accuracy_threshold | 10.5544 | | manhattan_f1 | 0.9333 | | manhattan_f1_threshold | 10.5544 | | manhattan_precision | 0.8974 | | manhattan_recall | 0.9722 | | manhattan_ap | 0.9614 | | euclidean_accuracy | 0.9424 | | euclidean_accuracy_threshold | 0.6556 | | euclidean_f1 | 0.9364 | | euclidean_f1_threshold | 0.6556 | | euclidean_precision | 0.9196 | | euclidean_recall | 0.9537 | | euclidean_ap | 0.9629 | | max_accuracy | 0.9424 | | max_accuracy_threshold | 10.5544 | | max_f1 | 0.9364 | | max_f1_threshold | 10.5544 | | max_precision | 0.9196 | | max_recall | 0.9722 | | **max_ap** | **0.9629** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 971 training samples * Columns: sentence2, sentence1, and label * Approximate statistics based on the first 1000 samples: | | sentence2 | sentence1 | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence2 | sentence1 | label | |:----------------------------------------------------------|:--------------------------------------------------------|:---------------| | Total number of bones in an adult human body | How many bones are in the human body? | 1 | | What is the largest river in North America? | What is the largest lake in North America? | 0 | | What is the capital of Australia? | What is the capital of New Zealand? | 0 | * Loss: [OnlineContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) ### Evaluation Dataset #### Unnamed Dataset * Size: 243 evaluation samples * Columns: sentence2, sentence1, and label * Approximate statistics based on the first 1000 samples: | | sentence2 | sentence1 | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence2 | sentence1 | label | |:-------------------------------------------------------------|:---------------------------------------------------------------|:---------------| | What are the various forms of renewable energy? | What are the different types of renewable energy? | 1 | | Gravity discoverer | Who discovered gravity? | 1 | | Can you help me write this report? | Can you help me understand this report? | 0 | * Loss: [OnlineContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `gradient_accumulation_steps`: 2 - `learning_rate`: 3e-06 - `weight_decay`: 0.01 - `num_train_epochs`: 20 - `lr_scheduler_type`: reduce_lr_on_plateau - `warmup_ratio`: 0.1 - `load_best_model_at_end`: True - `optim`: adamw_torch_fused #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 2 - `eval_accumulation_steps`: None - `learning_rate`: 3e-06 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 20 - `max_steps`: -1 - `lr_scheduler_type`: reduce_lr_on_plateau - `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`: 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} - `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`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | loss | pair-class-dev_max_ap | pair-class-test_max_ap | |:-----------:|:-------:|:----------:|:---------------------:|:----------------------:| | 0 | 0 | - | 0.6426 | - | | 0.9677 | 15 | 3.1481 | 0.7843 | - | | 2.0 | 31 | 2.1820 | 0.8692 | - | | 2.9677 | 46 | 1.8185 | 0.9078 | - | | 4.0 | 62 | 1.5769 | 0.9252 | - | | 4.9677 | 77 | 1.4342 | 0.9310 | - | | 6.0 | 93 | 1.3544 | 0.9357 | - | | 6.9677 | 108 | 1.2630 | 0.9402 | - | | 8.0 | 124 | 1.2120 | 0.9444 | - | | 8.9677 | 139 | 1.1641 | 0.9454 | - | | 10.0 | 155 | 1.0481 | 0.9464 | - | | 10.9677 | 170 | 0.9324 | 0.9509 | - | | 12.0 | 186 | 0.8386 | 0.9556 | - | | 12.9677 | 201 | 0.7930 | 0.9577 | - | | 14.0 | 217 | 0.7564 | 0.9599 | - | | 14.9677 | 232 | 0.7480 | 0.9606 | - | | 16.0 | 248 | 0.6733 | 0.9614 | - | | 16.9677 | 263 | 0.6434 | 0.9621 | - | | 18.0 | 279 | 0.6411 | 0.9630 | - | | 18.9677 | 294 | 0.6383 | 0.9632 | - | | **19.3548** | **300** | **0.6365** | **0.9629** | **0.9629** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.32.1 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## 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", } ```