--- base_model: sentence-transformers/all-mpnet-base-v2 library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:48393 - loss:MultipleNegativesRankingLoss widget: - source_sentence: Tennis champ Rafael Nadal lunges to return a ball. sentences: - The tennis champ has decided to quit playing tennis. - A woman stands alone at a restaurant. - A blond woman running - source_sentence: Small girl getting her face painted. sentences: - A Meijer in Illinois selling groceries. - Two men are posing together. - A small girl washing her face. - source_sentence: because too too often they're can be extremism that that hurts from from any direction regardless of whatever whatever you're arguing or concerned about and sentences: - If you could stir the mothers, you are done. - Extremism is bad. - Steve Ballmer is a college friend of mine. - source_sentence: The dog jumps over the log with a stick in its mouth. sentences: - A girl in red jumps outdoors. - The dog is running around with something in it's mouth. - The price is lower than what they pay. - source_sentence: A man in black shirt sits on a stool while trying to sell stuffed animals. sentences: - A man is sitting on a stool. - A pooch runs through the grass. - A young lady is sitting on a bench at the bus stop. model-index: - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 results: - task: type: information-retrieval name: Information Retrieval dataset: name: eval type: eval metrics: - type: cosine_accuracy@1 value: 0.0004959394953815635 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.36964023722439193 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.4739321802740066 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5881015849399707 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.0004959394953815635 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.12321341240813066 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09478643605480129 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05881015849399707 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0004959394953815635 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.36964023722439193 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4739321802740066 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5881015849399707 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3037659752455345 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2120033429995685 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.22559046634335145 name: Cosine Map@100 - type: dot_accuracy@1 value: 0.0005579319323042589 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.3696609013700329 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.4739321802740066 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.5881429132312525 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.0005579319323042589 name: Dot Precision@1 - type: dot_precision@3 value: 0.12322030045667762 name: Dot Precision@3 - type: dot_precision@5 value: 0.09478643605480132 name: Dot Precision@5 - type: dot_precision@10 value: 0.05881429132312524 name: Dot Precision@10 - type: dot_recall@1 value: 0.0005579319323042589 name: Dot Recall@1 - type: dot_recall@3 value: 0.3696609013700329 name: Dot Recall@3 - type: dot_recall@5 value: 0.4739321802740066 name: Dot Recall@5 - type: dot_recall@10 value: 0.5881429132312525 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.30380430047413587 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.2120435150827015 name: Dot Mrr@10 - type: dot_map@100 value: 0.22562658480145822 name: Dot Map@100 --- # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - **Maximum Sequence Length:** 384 tokens - **Output Dimensionality:** 768 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, '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("richie-ghost/sentence-transformers-all-mpnet-base-v2") # Run inference sentences = [ 'A man in black shirt sits on a stool while trying to sell stuffed animals.', 'A man is sitting on a stool.', 'A young lady is sitting on a bench at the bus stop.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `eval` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0005 | | cosine_accuracy@3 | 0.3696 | | cosine_accuracy@5 | 0.4739 | | cosine_accuracy@10 | 0.5881 | | cosine_precision@1 | 0.0005 | | cosine_precision@3 | 0.1232 | | cosine_precision@5 | 0.0948 | | cosine_precision@10 | 0.0588 | | cosine_recall@1 | 0.0005 | | cosine_recall@3 | 0.3696 | | cosine_recall@5 | 0.4739 | | cosine_recall@10 | 0.5881 | | cosine_ndcg@10 | 0.3038 | | cosine_mrr@10 | 0.212 | | cosine_map@100 | 0.2256 | | dot_accuracy@1 | 0.0006 | | dot_accuracy@3 | 0.3697 | | dot_accuracy@5 | 0.4739 | | dot_accuracy@10 | 0.5881 | | dot_precision@1 | 0.0006 | | dot_precision@3 | 0.1232 | | dot_precision@5 | 0.0948 | | dot_precision@10 | 0.0588 | | dot_recall@1 | 0.0006 | | dot_recall@3 | 0.3697 | | dot_recall@5 | 0.4739 | | dot_recall@10 | 0.5881 | | dot_ndcg@10 | 0.3038 | | dot_mrr@10 | 0.212 | | **dot_map@100** | **0.2256** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 48,393 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:---------------------------------------------------------------------|:------------------------------------------------------------------| | A group of kids in red and white playing soccer. | There are kids playing ball in a soccer tournament. | | I had a great time at the theme park with my family. | Did you have fun at the theme park with your family? | | A black and white elderly gentlemen riding an am-track. | A man is on a train. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 4 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `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`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `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`: False - `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 - `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 - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | Training Loss | eval_dot_map@100 | |:------:|:-----:|:-------------:|:----------------:| | 0.1653 | 500 | 0.0446 | 0.2186 | | 0.3306 | 1000 | 0.0544 | 0.2226 | | 0.4959 | 1500 | 0.0419 | 0.2191 | | 0.6612 | 2000 | 0.0532 | 0.2210 | | 0.8264 | 2500 | 0.0438 | 0.2209 | | 0.9917 | 3000 | 0.0422 | 0.2220 | | 1.0 | 3025 | - | 0.2225 | | 1.1570 | 3500 | 0.021 | 0.2236 | | 1.3223 | 4000 | 0.0163 | 0.2243 | | 1.4876 | 4500 | 0.0158 | 0.2221 | | 1.6529 | 5000 | 0.0178 | 0.2221 | | 1.8182 | 5500 | 0.0154 | 0.2222 | | 1.9835 | 6000 | 0.0145 | 0.2228 | | 2.0 | 6050 | - | 0.2247 | | 2.1488 | 6500 | 0.0098 | 0.2250 | | 2.3140 | 7000 | 0.0076 | 0.2239 | | 2.4793 | 7500 | 0.0069 | 0.2253 | | 2.6446 | 8000 | 0.0073 | 0.2245 | | 2.8099 | 8500 | 0.0063 | 0.2245 | | 2.9752 | 9000 | 0.0074 | 0.2251 | | 3.0 | 9075 | - | 0.2251 | | 3.1405 | 9500 | 0.0044 | 0.2256 | | 3.3058 | 10000 | 0.0043 | 0.2259 | | 3.4711 | 10500 | 0.0038 | 0.2261 | | 3.6364 | 11000 | 0.0039 | 0.2256 | | 3.8017 | 11500 | 0.0037 | 0.2251 | | 3.9669 | 12000 | 0.0043 | 0.2256 | | 4.0 | 12100 | - | 0.2256 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.2.1 - Transformers: 4.44.2 - PyTorch: 2.5.0+cu121 - Accelerate: 1.0.1 - Datasets: 3.0.2 - 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```