--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:557850 - loss:MultipleNegativesRankingLoss base_model: microsoft/mpnet-base 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. datasets: - sentence-transformers/all-nli pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy 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: 0.903857837181045 name: Cosine Accuracy - task: type: triplet name: Triplet dataset: name: all nli test type: all-nli-test metrics: - type: cosine_accuracy value: 0.9154183688909063 name: Cosine 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 dimensions - **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("jangikim/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 * Datasets: `all-nli-dev` and `all-nli-test` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | all-nli-dev | all-nli-test | |:--------------------|:------------|:-------------| | **cosine_accuracy** | **0.9039** | **0.9154** | ## 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 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `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`: 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`: 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`: True - `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 - `include_for_metrics`: [] - `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 - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | all-nli-dev_cosine_accuracy | all-nli-test_cosine_accuracy | |:-----:|:----:|:-------------:|:---------------:|:---------------------------:|:----------------------------:| | 0 | 0 | - | - | 0.6211 | - | | 0.016 | 100 | 2.5306 | 1.0656 | 0.7749 | - | | 0.032 | 200 | 0.9109 | 0.8554 | 0.7936 | - | | 0.048 | 300 | 1.2488 | 0.8116 | 0.8045 | - | | 0.064 | 400 | 0.7921 | 0.8638 | 0.7980 | - | | 0.08 | 500 | 0.7285 | 1.0676 | 0.7693 | - | | 0.096 | 600 | 0.9519 | 1.2276 | 0.7673 | - | | 0.112 | 700 | 0.8569 | 1.2144 | 0.7749 | - | | 0.128 | 800 | 1.3088 | 1.3994 | 0.7555 | - | | 0.144 | 900 | 1.2227 | 1.4842 | 0.7570 | - | | 0.16 | 1000 | 1.104 | 1.1708 | 0.7629 | - | | 0.176 | 1100 | 1.069 | 1.3670 | 0.7577 | - | | 0.192 | 1200 | 0.9874 | 1.4517 | 0.7394 | - | | 0.208 | 1300 | 0.7999 | 1.2141 | 0.7518 | - | | 0.224 | 1400 | 0.776 | 1.2913 | 0.7605 | - | | 0.24 | 1500 | 1.0367 | 1.0660 | 0.7743 | - | | 0.256 | 1600 | 0.6614 | 1.1335 | 0.7631 | - | | 0.272 | 1700 | 1.0519 | 1.9327 | 0.7022 | - | | 0.288 | 1800 | 1.1647 | 1.2847 | 0.7503 | - | | 0.304 | 1900 | 0.8315 | 1.1214 | 0.7547 | - | | 0.32 | 2000 | 0.6953 | 1.0206 | 0.8094 | - | | 0.336 | 2100 | 0.6189 | 1.0757 | 0.8176 | - | | 0.352 | 2200 | 0.6519 | 1.0730 | 0.8202 | - | | 0.368 | 2300 | 0.9357 | 1.5665 | 0.7749 | - | | 0.384 | 2400 | 1.1421 | 1.1033 | 0.7948 | - | | 0.4 | 2500 | 0.898 | 1.2376 | 0.7795 | - | | 0.416 | 2600 | 0.6352 | 0.9549 | 0.8237 | - | | 0.432 | 2700 | 0.8724 | 1.2148 | 0.8085 | - | | 0.448 | 2800 | 1.5489 | 0.9826 | 0.8111 | - | | 0.464 | 2900 | 0.8694 | 0.9075 | 0.8202 | - | | 0.48 | 3000 | 0.7603 | 0.8855 | 0.8392 | - | | 0.496 | 3100 | 0.832 | 0.8339 | 0.8389 | - | | 0.512 | 3200 | 0.6681 | 0.8775 | 0.8474 | - | | 0.528 | 3300 | 0.6928 | 0.7839 | 0.8666 | - | | 0.544 | 3400 | 0.5855 | 0.8005 | 0.8540 | - | | 0.56 | 3500 | 0.5602 | 0.8667 | 0.8530 | - | | 0.576 | 3600 | 0.6113 | 0.7388 | 0.8490 | - | | 0.592 | 3700 | 0.5827 | 0.7075 | 0.8609 | - | | 0.608 | 3800 | 0.5542 | 0.6796 | 0.8738 | - | | 0.624 | 3900 | 0.5551 | 0.7380 | 0.8659 | - | | 0.64 | 4000 | 0.7671 | 0.7355 | 0.8680 | - | | 0.656 | 4100 | 0.9996 | 0.7832 | 0.8791 | - | | 0.672 | 4200 | 0.9447 | 0.6966 | 0.8835 | - | | 0.688 | 4300 | 0.722 | 0.6668 | 0.8896 | - | | 0.704 | 4400 | 0.6671 | 0.6204 | 0.8899 | - | | 0.72 | 4500 | 0.5729 | 0.5900 | 0.8818 | - | | 0.736 | 4600 | 0.6538 | 0.5833 | 0.8900 | - | | 0.752 | 4700 | 0.6969 | 0.6433 | 0.8862 | - | | 0.768 | 4800 | 0.6354 | 0.5750 | 0.8905 | - | | 0.784 | 4900 | 0.5742 | 0.5635 | 0.8897 | - | | 0.8 | 5000 | 0.6725 | 0.6278 | 0.8900 | - | | 0.816 | 5100 | 0.5477 | 0.5660 | 0.8906 | - | | 0.832 | 5200 | 0.5927 | 0.5440 | 0.8944 | - | | 0.848 | 5300 | 0.5112 | 0.5509 | 0.8975 | - | | 0.864 | 5400 | 0.6042 | 0.5706 | 0.8950 | - | | 0.88 | 5500 | 0.5593 | 0.5485 | 0.8928 | - | | 0.896 | 5600 | 0.5597 | 0.5399 | 0.9005 | - | | 0.912 | 5700 | 0.628 | 0.5356 | 0.8996 | - | | 0.928 | 5800 | 0.5313 | 0.5115 | 0.8981 | - | | 0.944 | 5900 | 0.7392 | 0.5187 | 0.8985 | - | | 0.96 | 6000 | 0.7582 | 0.5322 | 0.9031 | - | | 0.976 | 6100 | 0.6313 | 0.5243 | 0.9033 | - | | 0.992 | 6200 | 0.0004 | 0.5232 | 0.9039 | - | | 1.0 | 6250 | - | - | - | 0.9154 | ### Framework Versions - Python: 3.10.15 - Sentence Transformers: 3.3.1 - Transformers: 4.46.3 - PyTorch: 2.5.1+cu124 - Accelerate: 1.1.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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} } ```