--- base_model: microsoft/mpnet-base datasets: - sentence-transformers/all-nli language: - en library_name: sentence-transformers license: apache-2.0 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:557850 - loss:MultipleNegativesRankingLoss widget: - source_sentence: A man is jumping unto his filthy bed. sentences: - A young male is looking at a newspaper while 2 females walks past him. - The bed is dirty. - The man is on the moon. - source_sentence: A carefully balanced male stands on one foot near a clean ocean beach area. sentences: - A man is ouside near the beach. - Three policemen patrol the streets on bikes - A man is sitting on his couch. - source_sentence: The man is wearing a blue shirt. sentences: - Near the trashcan the man stood and smoked - A man in a blue shirt leans on a wall beside a road with a blue van and red car with water in the background. - A man in a black shirt is playing a guitar. - source_sentence: The girls are outdoors. sentences: - Two girls riding on an amusement part ride. - a guy laughs while doing laundry - Three girls are standing together in a room, one is listening, one is writing on a wall and the third is talking to them. - source_sentence: A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling. sentences: - A worker is looking out of a manhole. - A man is giving a presentation. - The workers are both inside the manhole. model-index: - name: MPNet base trained on AllNLI triplets results: - task: type: triplet name: Triplet dataset: name: all nli dev type: all-nli-dev metrics: - type: cosine_accuracy value: 0.9141859052247874 name: Cosine Accuracy - type: dot_accuracy value: 0.08444714459295262 name: Dot Accuracy - type: manhattan_accuracy value: 0.9097812879708383 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.9097812879708383 name: Euclidean Accuracy - type: max_accuracy value: 0.9141859052247874 name: Max Accuracy - task: type: triplet name: Triplet dataset: name: all nli test type: all-nli-test metrics: - type: cosine_accuracy value: 0.926463912846119 name: Cosine Accuracy - type: dot_accuracy value: 0.07353608715388107 name: Dot Accuracy - type: manhattan_accuracy value: 0.9187471629596006 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.9179906188530791 name: Euclidean Accuracy - type: max_accuracy value: 0.926463912846119 name: Max Accuracy --- # MPNet base trained on AllNLI triplets This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) - **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': 512, '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}) ) ``` ## 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("chanbistec/mpnet-base-all-nli-triplet") # Run inference sentences = [ 'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.', 'A worker is looking out of a manhole.', 'The workers are both inside the manhole.', ] 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 #### Triplet * Dataset: `all-nli-dev` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:-------------------|:-----------| | cosine_accuracy | 0.9142 | | dot_accuracy | 0.0844 | | manhattan_accuracy | 0.9098 | | euclidean_accuracy | 0.9098 | | **max_accuracy** | **0.9142** | #### Triplet * Dataset: `all-nli-test` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:-------------------|:-----------| | cosine_accuracy | 0.9265 | | dot_accuracy | 0.0735 | | manhattan_accuracy | 0.9187 | | euclidean_accuracy | 0.918 | | **max_accuracy** | **0.9265** | ## Training Details ### Training Dataset #### all-nli * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 557,850 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 | |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| | A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | A person is at a diner, ordering an omelette. | | Children smiling and waving at camera | There are children present | The kids are frowning | | A boy is jumping on skateboard in the middle of a red bridge. | The boy does a skateboarding trick. | The boy skates down the sidewalk. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### all-nli * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 6,584 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 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------| | Two women are embracing while holding to go packages. | Two woman are holding packages. | The men are fighting outside a deli. | | Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink. | Two kids in numbered jerseys wash their hands. | Two kids in jackets walk to school. | | A man selling donuts to a customer during a world exhibition event held in the city of Angeles | A man selling donuts to a customer. | A woman drinks her coffee in a small cafe. | * 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 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `batch_sampler`: no_duplicates #### 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`: 2e-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`: 1 - `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`: 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`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy | |:-----:|:----:|:-------------:|:------:|:------------------------:|:-------------------------:| | 0 | 0 | - | - | 0.6832 | - | | 0.016 | 100 | 3.0282 | 1.5782 | 0.7752 | - | | 0.032 | 200 | 1.2529 | 0.9154 | 0.7991 | - | | 0.048 | 300 | 1.4472 | 0.7901 | 0.8103 | - | | 0.064 | 400 | 0.9059 | 0.7468 | 0.8114 | - | | 0.08 | 500 | 0.8663 | 0.8423 | 0.7981 | - | | 0.096 | 600 | 1.0836 | 0.8995 | 0.8010 | - | | 0.112 | 700 | 0.9315 | 0.8971 | 0.8100 | - | | 0.128 | 800 | 1.1273 | 0.9654 | 0.8012 | - | | 0.144 | 900 | 1.1194 | 0.9318 | 0.8303 | - | | 0.16 | 1000 | 1.0911 | 0.9048 | 0.8038 | - | | 0.176 | 1100 | 1.1332 | 0.9340 | 0.8039 | - | | 0.192 | 1200 | 1.0154 | 0.9041 | 0.8076 | - | | 0.208 | 1300 | 0.7995 | 0.9301 | 0.7959 | - | | 0.224 | 1400 | 0.7614 | 0.8275 | 0.8071 | - | | 0.24 | 1500 | 0.8724 | 0.7973 | 0.8173 | - | | 0.256 | 1600 | 0.6751 | 0.7916 | 0.8197 | - | | 0.272 | 1700 | 0.8933 | 0.8572 | 0.8194 | - | | 0.288 | 1800 | 0.8585 | 0.8560 | 0.8056 | - | | 0.304 | 1900 | 0.8354 | 0.7987 | 0.8123 | - | | 0.32 | 2000 | 0.7484 | 0.7559 | 0.8348 | - | | 0.336 | 2100 | 0.6047 | 0.7532 | 0.8471 | - | | 0.352 | 2200 | 0.6221 | 0.6956 | 0.8665 | - | | 0.368 | 2300 | 0.8332 | 0.7214 | 0.8542 | - | | 0.384 | 2400 | 0.7755 | 0.7007 | 0.8481 | - | | 0.4 | 2500 | 0.6912 | 0.7505 | 0.8499 | - | | 0.416 | 2600 | 0.6169 | 0.6536 | 0.8591 | - | | 0.432 | 2700 | 0.8907 | 0.7240 | 0.8560 | - | | 0.448 | 2800 | 0.8576 | 0.6790 | 0.8499 | - | | 0.464 | 2900 | 0.8057 | 0.6870 | 0.8575 | - | | 0.48 | 3000 | 0.6928 | 0.6540 | 0.8641 | - | | 0.496 | 3100 | 0.7566 | 0.6419 | 0.8682 | - | | 0.512 | 3200 | 0.5757 | 0.6109 | 0.8783 | - | | 0.528 | 3300 | 0.601 | 0.5481 | 0.8914 | - | | 0.544 | 3400 | 0.5105 | 0.5853 | 0.8820 | - | | 0.56 | 3500 | 0.5116 | 0.5918 | 0.8961 | - | | 0.576 | 3600 | 0.495 | 0.5546 | 0.8897 | - | | 0.592 | 3700 | 0.5585 | 0.5457 | 0.8970 | - | | 0.608 | 3800 | 0.4778 | 0.5056 | 0.9020 | - | | 0.624 | 3900 | 0.5116 | 0.5203 | 0.9019 | - | | 0.64 | 4000 | 0.753 | 0.5490 | 0.9019 | - | | 0.656 | 4100 | 0.9207 | 0.5447 | 0.9049 | - | | 0.672 | 4200 | 0.8695 | 0.4996 | 0.9055 | - | | 0.688 | 4300 | 0.6867 | 0.4825 | 0.9107 | - | | 0.704 | 4400 | 0.5961 | 0.4670 | 0.9166 | - | | 0.72 | 4500 | 0.5547 | 0.4748 | 0.9104 | - | | 0.736 | 4600 | 0.6145 | 0.4636 | 0.9145 | - | | 0.752 | 4700 | 0.6643 | 0.4806 | 0.9128 | - | | 0.768 | 4800 | 0.6134 | 0.4521 | 0.9110 | - | | 0.784 | 4900 | 0.5847 | 0.4627 | 0.9080 | - | | 0.8 | 5000 | 0.6482 | 0.4853 | 0.9107 | - | | 0.816 | 5100 | 0.5103 | 0.4374 | 0.9104 | - | | 0.832 | 5200 | 0.5639 | 0.4306 | 0.9089 | - | | 0.848 | 5300 | 0.5247 | 0.4418 | 0.9116 | - | | 0.864 | 5400 | 0.6094 | 0.4564 | 0.9101 | - | | 0.88 | 5500 | 0.5296 | 0.4394 | 0.9092 | - | | 0.896 | 5600 | 0.5469 | 0.4316 | 0.9101 | - | | 0.912 | 5700 | 0.6061 | 0.4258 | 0.9124 | - | | 0.928 | 5800 | 0.5456 | 0.4167 | 0.9113 | - | | 0.944 | 5900 | 0.6776 | 0.4168 | 0.9108 | - | | 0.96 | 6000 | 0.7401 | 0.4267 | 0.9139 | - | | 0.976 | 6100 | 0.6568 | 0.4227 | 0.9140 | - | | 0.992 | 6200 | 0.0002 | 0.4224 | 0.9142 | - | | 1.0 | 6250 | - | - | - | 0.9265 | ### Framework Versions - Python: 3.12.4 - Sentence Transformers: 3.1.0 - Transformers: 4.44.2 - PyTorch: 2.4.1 - Accelerate: 0.34.2 - Datasets: 3.0.0 - 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} } ```