--- base_model: intfloat/multilingual-e5-small datasets: [] language: [] library_name: sentence-transformers metrics: - cosine_accuracy - dot_accuracy - manhattan_accuracy - euclidean_accuracy - max_accuracy pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:548 - loss:TripletLoss widget: - source_sentence: What's the best way to learn a new language? sentences: - What is the fastest way to travel? - Current CEO of Apple Inc. - Best methods to learn a new language - source_sentence: Where can I find the best sushi in town? sentences: - Paracetamol side effects - Where can I find the best pizza in town? - Where can I find the best sushi nearby? - source_sentence: How to bake a chocolate cake? sentences: - How to make chocolate chip cookies? - Signs and symptoms of anxiety - Steps to bake a chocolate cake - source_sentence: What is the largest lake in North America? sentences: - Steps to cook pasta - What is the largest river in North America? - North America's largest lake by area - source_sentence: How many countries are in the European Union? sentences: - Formula to find the area of a circle - Number of countries in the European Union - How many continents are there? model-index: - name: SentenceTransformer based on intfloat/multilingual-e5-small results: - task: type: triplet name: Triplet dataset: name: triplet validation type: triplet-validation metrics: - type: cosine_accuracy value: 1.0 name: Cosine Accuracy - type: dot_accuracy value: 0.0 name: Dot Accuracy - type: manhattan_accuracy value: 1.0 name: Manhattan Accuracy - type: euclidean_accuracy value: 1.0 name: Euclidean Accuracy - type: max_accuracy value: 1.0 name: Max Accuracy --- # 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/e-small-triplet") # Run inference sentences = [ 'How many countries are in the European Union?', 'Number of countries in the European Union', 'How many continents are there?', ] 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 #### Triplet * Dataset: `triplet-validation` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:-------------------|:--------| | cosine_accuracy | 1.0 | | dot_accuracy | 0.0 | | manhattan_accuracy | 1.0 | | euclidean_accuracy | 1.0 | | **max_accuracy** | **1.0** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 548 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:-------------------------------------------------------------------|:------------------------------------------------------|:-----------------------------------------------------------------------| | What is the difference between a laptop and a tablet? | Comparison between a laptop and a tablet | What is the difference between a laptop and a smartphone? | | How do I get to the nearest train station? | Directions to the nearest train station | How do I get to the airport? | | Who is the author of '1984'? | Writer of the novel '1984' | Who is the author of 'Pride and Prejudice'? | * Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 0.5 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 61 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:-------------------------------------------------------|:---------------------------------------------------------|:--------------------------------------------------------| | How many states are there in the USA? | Total number of states in the United States | How many provinces are there in Canada? | | What is the chemical formula for ethanol? | Molecular structure of ethanol | What is the chemical formula for methanol? | | How to clean a laptop screen? | Steps to safely clean a laptop display | How to clean a laptop keyboard? | * Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 0.5 } ``` ### 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-05 - `weight_decay`: 0.01 - `num_train_epochs`: 10 - `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-05 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 10 - `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 | Training Loss | loss | triplet-validation_max_accuracy | |:-------:|:------:|:-------------:|:----------:|:-------------------------------:| | 1.0 | 9 | - | 0.1078 | - | | 1.1111 | 10 | 0.3352 | - | - | | 2.0 | 18 | - | 0.0670 | - | | 2.2222 | 20 | 0.1677 | - | - | | 3.0 | 27 | - | 0.0434 | - | | 3.3333 | 30 | 0.0832 | - | - | | 4.0 | 36 | - | 0.0323 | - | | 4.4444 | 40 | 0.063 | - | - | | 5.0 | 45 | - | 0.0299 | - | | 5.5556 | 50 | 0.0449 | - | - | | 6.0 | 54 | - | 0.0273 | - | | 6.6667 | 60 | 0.0357 | - | - | | 7.0 | 63 | - | 0.0241 | - | | 7.7778 | 70 | 0.0254 | - | - | | 8.0 | 72 | - | 0.0224 | - | | 8.8889 | 80 | 0.02 | - | - | | **9.0** | **81** | **-** | **0.0211** | **-** | | 10.0 | 90 | 0.0173 | 0.0216 | 1.0 | * 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", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```