--- base_model: intfloat/multilingual-e5-small 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:2476 - loss:OnlineContrastiveLoss widget: - source_sentence: Would you want to be President? sentences: - Can you help me with my homework? - How to bake cookies? - Why do you want to be to president? - source_sentence: Velocity of sound waves in the atmosphere sentences: - What is the speed of sound in air? - What is the best/most memorable thing you've ever eaten and why? - The `safe` option in the `to_spreadsheet` method controls whether a safe conversion or not is needed for certain plant attributes to store the data in a SpreadsheetTable or Row. - source_sentence: Number of countries in the European Union sentences: - How many countries are in the European Union? - Who painted the Sistine Chapel ceiling? - The RecipeManager class is used to manage the downloading and extraction of recipes. - source_sentence: Official currency of the USA sentences: - What is purpose of life? - Files inside ZIP archives are accessed and yielded sequentially using iter_zip(). - What is the currency of the United States? - source_sentence: Who wrote the book "1984"? sentences: - What is the speed of light? - How to set up a home gym? - Who wrote the book "To Kill a Mockingbird"? 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: 0.8623188405797102 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.8491722345352173 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.8856304985337243 name: Cosine F1 - type: cosine_f1_threshold value: 0.8245140314102173 name: Cosine F1 Threshold - type: cosine_precision value: 0.8435754189944135 name: Cosine Precision - type: cosine_recall value: 0.9320987654320988 name: Cosine Recall - type: cosine_ap value: 0.9266759550807792 name: Cosine Ap - type: dot_accuracy value: 0.8623188405797102 name: Dot Accuracy - type: dot_accuracy_threshold value: 0.8491722941398621 name: Dot Accuracy Threshold - type: dot_f1 value: 0.8856304985337243 name: Dot F1 - type: dot_f1_threshold value: 0.8245140910148621 name: Dot F1 Threshold - type: dot_precision value: 0.8435754189944135 name: Dot Precision - type: dot_recall value: 0.9320987654320988 name: Dot Recall - type: dot_ap value: 0.9266759550807792 name: Dot Ap - type: manhattan_accuracy value: 0.8623188405797102 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 8.599637031555176 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.8856304985337243 name: Manhattan F1 - type: manhattan_f1_threshold value: 9.221129417419434 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.8435754189944135 name: Manhattan Precision - type: manhattan_recall value: 0.9320987654320988 name: Manhattan Recall - type: manhattan_ap value: 0.9260061788962293 name: Manhattan Ap - type: euclidean_accuracy value: 0.8623188405797102 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 0.5491920709609985 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.8856304985337243 name: Euclidean F1 - type: euclidean_f1_threshold value: 0.5924187898635864 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.8435754189944135 name: Euclidean Precision - type: euclidean_recall value: 0.9320987654320988 name: Euclidean Recall - type: euclidean_ap value: 0.9266759550807792 name: Euclidean Ap - type: max_accuracy value: 0.8623188405797102 name: Max Accuracy - type: max_accuracy_threshold value: 8.599637031555176 name: Max Accuracy Threshold - type: max_f1 value: 0.8856304985337243 name: Max F1 - type: max_f1_threshold value: 9.221129417419434 name: Max F1 Threshold - type: max_precision value: 0.8435754189944135 name: Max Precision - type: max_recall value: 0.9320987654320988 name: Max Recall - type: max_ap value: 0.9266759550807792 name: Max Ap - task: type: binary-classification name: Binary Classification dataset: name: pair class test type: pair-class-test metrics: - type: cosine_accuracy value: 0.8659420289855072 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.8320531249046326 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.8875379939209727 name: Cosine F1 - type: cosine_f1_threshold value: 0.8320531249046326 name: Cosine F1 Threshold - type: cosine_precision value: 0.874251497005988 name: Cosine Precision - type: cosine_recall value: 0.9012345679012346 name: Cosine Recall - type: cosine_ap value: 0.9257692996006023 name: Cosine Ap - type: dot_accuracy value: 0.8659420289855072 name: Dot Accuracy - type: dot_accuracy_threshold value: 0.8320530652999878 name: Dot Accuracy Threshold - type: dot_f1 value: 0.8875379939209727 name: Dot F1 - type: dot_f1_threshold value: 0.8320530652999878 name: Dot F1 Threshold - type: dot_precision value: 0.874251497005988 name: Dot Precision - type: dot_recall value: 0.9012345679012346 name: Dot Recall - type: dot_ap value: 0.9257692996006023 name: Dot Ap - type: manhattan_accuracy value: 0.8623188405797102 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 8.854782104492188 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.8875739644970415 name: Manhattan F1 - type: manhattan_f1_threshold value: 9.349273681640625 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.8522727272727273 name: Manhattan Precision - type: manhattan_recall value: 0.9259259259259259 name: Manhattan Recall - type: manhattan_ap value: 0.9255387736459155 name: Manhattan Ap - type: euclidean_accuracy value: 0.8659420289855072 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 0.5795620679855347 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.8875379939209727 name: Euclidean F1 - type: euclidean_f1_threshold value: 0.5795620679855347 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.874251497005988 name: Euclidean Precision - type: euclidean_recall value: 0.9012345679012346 name: Euclidean Recall - type: euclidean_ap value: 0.9257692996006023 name: Euclidean Ap - type: max_accuracy value: 0.8659420289855072 name: Max Accuracy - type: max_accuracy_threshold value: 8.854782104492188 name: Max Accuracy Threshold - type: max_f1 value: 0.8875739644970415 name: Max F1 - type: max_f1_threshold value: 9.349273681640625 name: Max F1 Threshold - type: max_precision value: 0.874251497005988 name: Max Precision - type: max_recall value: 0.9259259259259259 name: Max Recall - type: max_ap value: 0.9257692996006023 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/fine_tuned_model_9") # Run inference sentences = [ 'Who wrote the book "1984"?', 'Who wrote the book "To Kill a Mockingbird"?', 'What is the speed of light?', ] 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 | 0.8623 | | cosine_accuracy_threshold | 0.8492 | | cosine_f1 | 0.8856 | | cosine_f1_threshold | 0.8245 | | cosine_precision | 0.8436 | | cosine_recall | 0.9321 | | cosine_ap | 0.9267 | | dot_accuracy | 0.8623 | | dot_accuracy_threshold | 0.8492 | | dot_f1 | 0.8856 | | dot_f1_threshold | 0.8245 | | dot_precision | 0.8436 | | dot_recall | 0.9321 | | dot_ap | 0.9267 | | manhattan_accuracy | 0.8623 | | manhattan_accuracy_threshold | 8.5996 | | manhattan_f1 | 0.8856 | | manhattan_f1_threshold | 9.2211 | | manhattan_precision | 0.8436 | | manhattan_recall | 0.9321 | | manhattan_ap | 0.926 | | euclidean_accuracy | 0.8623 | | euclidean_accuracy_threshold | 0.5492 | | euclidean_f1 | 0.8856 | | euclidean_f1_threshold | 0.5924 | | euclidean_precision | 0.8436 | | euclidean_recall | 0.9321 | | euclidean_ap | 0.9267 | | max_accuracy | 0.8623 | | max_accuracy_threshold | 8.5996 | | max_f1 | 0.8856 | | max_f1_threshold | 9.2211 | | max_precision | 0.8436 | | max_recall | 0.9321 | | **max_ap** | **0.9267** | #### 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 | 0.8659 | | cosine_accuracy_threshold | 0.8321 | | cosine_f1 | 0.8875 | | cosine_f1_threshold | 0.8321 | | cosine_precision | 0.8743 | | cosine_recall | 0.9012 | | cosine_ap | 0.9258 | | dot_accuracy | 0.8659 | | dot_accuracy_threshold | 0.8321 | | dot_f1 | 0.8875 | | dot_f1_threshold | 0.8321 | | dot_precision | 0.8743 | | dot_recall | 0.9012 | | dot_ap | 0.9258 | | manhattan_accuracy | 0.8623 | | manhattan_accuracy_threshold | 8.8548 | | manhattan_f1 | 0.8876 | | manhattan_f1_threshold | 9.3493 | | manhattan_precision | 0.8523 | | manhattan_recall | 0.9259 | | manhattan_ap | 0.9255 | | euclidean_accuracy | 0.8659 | | euclidean_accuracy_threshold | 0.5796 | | euclidean_f1 | 0.8875 | | euclidean_f1_threshold | 0.5796 | | euclidean_precision | 0.8743 | | euclidean_recall | 0.9012 | | euclidean_ap | 0.9258 | | max_accuracy | 0.8659 | | max_accuracy_threshold | 8.8548 | | max_f1 | 0.8876 | | max_f1_threshold | 9.3493 | | max_precision | 0.8743 | | max_recall | 0.9259 | | **max_ap** | **0.9258** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 2,476 training samples * Columns: sentence2, label, and sentence1 * Approximate statistics based on the first 1000 samples: | | sentence2 | label | sentence1 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | int | string | | details | | | | * Samples: | sentence2 | label | sentence1 | |:--------------------------------------------------------------------------------------------------------|:---------------|:----------------------------------------------------------------------------------------------------------| | A model is trained using the ImageNet dataset to classify images into distinct categories. | 1 | The ImageNet dataset is used for training models to classify images into various categories. | | Version 5.3.1 does not contain it. | 1 | No, it doesn't exist in version 5.3.1. | | Can you do my homework for me? | 0 | Can you help me with my homework? | * Loss: [OnlineContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) ### Evaluation Dataset #### Unnamed Dataset * Size: 276 evaluation samples * Columns: sentence2, label, and sentence1 * Approximate statistics based on the first 276 samples: | | sentence2 | label | sentence1 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | int | string | | details | | | | * Samples: | sentence2 | label | sentence1 | |:---------------------------------------------------------------------------------------------------------------------------|:---------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------| | How is AI used to enhance cybersecurity? | 0 | What are the challenges of AI in cybersecurity? | | The SYSTEM log documentation can be accessed by clicking on the link which will take you to the main version. | 1 | You can find the SYSTEM log documentation on the main version. Click on the provided link to redirect to the main version of the documentation. | | Name the capital city of Italy | 1 | What is the capital of Italy? | * 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 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 - `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`: 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`: 5e-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`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `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`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | pair-class-dev_max_ap | pair-class-test_max_ap | |:-------:|:-------:|:-------------:|:----------:|:---------------------:|:----------------------:| | 0 | 0 | - | - | 0.7876 | - | | 0.2564 | 10 | 1.6257 | - | - | - | | 0.5128 | 20 | 0.8138 | - | - | - | | 0.7692 | 30 | 0.7276 | - | - | - | | 1.0 | 39 | - | 0.8190 | 0.9089 | - | | 1.0256 | 40 | 0.6423 | - | - | - | | 1.2821 | 50 | 0.5168 | - | - | - | | 1.5385 | 60 | 0.3583 | - | - | - | | 1.7949 | 70 | 0.3182 | - | - | - | | 2.0 | 78 | - | 0.7351 | 0.9215 | - | | 2.0513 | 80 | 0.3521 | - | - | - | | 2.3077 | 90 | 0.2037 | - | - | - | | 2.5641 | 100 | 0.1293 | - | - | - | | 2.8205 | 110 | 0.1374 | - | - | - | | **3.0** | **117** | **-** | **0.7223** | **0.9258** | **-** | | 3.0769 | 120 | 0.198 | - | - | - | | 3.3333 | 130 | 0.0667 | - | - | - | | 3.5897 | 140 | 0.0526 | - | - | - | | 3.8462 | 150 | 0.0652 | - | - | - | | 4.0 | 156 | - | 0.7327 | 0.9267 | 0.9258 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.1.0 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.34.2 - 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", } ```