| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:100000 |
| - 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: 0.8253341317176819 |
| name: Cosine Accuracy |
| - type: cosine_accuracy |
| value: 0.9163122773170471 |
| 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) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 --> |
| - **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("tien314/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] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Triplet |
|
|
| * Dataset: `all-nli-dev` |
| * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | **cosine_accuracy** | **0.8253** | |
| |
| #### Triplet |
| |
| * Dataset: `all-nli-dev` |
| * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
| |
| | Metric | Value | |
| |:--------------------|:-----------| |
| | **cosine_accuracy** | **0.9163** | |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## 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: 100,000 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 7 tokens</li><li>mean: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| |
| | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> | |
| | <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> | |
| | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](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: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | |
| |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 17.95 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.78 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.35 tokens</li><li>max: 29 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------| |
| | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> | |
| | <code>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.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> | |
| | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](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 |
| - `bf16`: True |
| - `batch_sampler`: no_duplicates |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `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`: True |
| - `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} |
| - `tp_size`: 0 |
| - `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`: None |
| - `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 |
| - `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 |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | all-nli-dev_cosine_accuracy | |
| |:-----:|:----:|:-------------:|:---------------:|:---------------------------:| |
| | -1 | -1 | - | - | 0.6211 | |
| | 0.016 | 100 | 2.7517 | 0.8461 | 0.7871 | |
| | 0.032 | 200 | 1.1987 | 0.6156 | 0.8253 | |
| | 0.048 | 300 | 0.9926 | - | - | |
| | -1 | -1 | - | - | 0.8302 | |
| | 0.016 | 100 | 0.8938 | 0.5516 | 0.8393 | |
| | 0.032 | 200 | 0.542 | 0.5312 | 0.8458 | |
| | 0.048 | 300 | 0.2881 | 0.5963 | 0.8288 | |
| | 0.064 | 400 | 0.954 | 0.5271 | 0.8518 | |
| | 0.08 | 500 | 0.8661 | 0.5029 | 0.8612 | |
| | 0.096 | 600 | 0.8477 | 0.5000 | 0.8688 | |
| | 0.112 | 700 | 0.8385 | 0.4942 | 0.8782 | |
| | 0.128 | 800 | 0.8051 | 0.4967 | 0.8890 | |
| | 0.144 | 900 | 0.7436 | 0.4855 | 0.8829 | |
| | 0.16 | 1000 | 0.6705 | 0.5059 | 0.8736 | |
| | 0.176 | 1100 | 0.7461 | 0.4721 | 0.8875 | |
| | 0.192 | 1200 | 0.6506 | 0.4403 | 0.8897 | |
| | 0.208 | 1300 | 0.6449 | 0.4410 | 0.8964 | |
| | 0.224 | 1400 | 0.6272 | 0.4310 | 0.8949 | |
| | 0.24 | 1500 | 0.6698 | 0.4382 | 0.8967 | |
| | 0.256 | 1600 | 0.624 | 0.3988 | 0.9060 | |
| | 0.272 | 1700 | 0.5965 | 0.4297 | 0.8917 | |
| | 0.288 | 1800 | 0.5652 | 0.4255 | 0.8995 | |
| | 0.304 | 1900 | 0.5301 | 0.4271 | 0.9042 | |
| | 0.32 | 2000 | 0.5132 | 0.4547 | 0.8985 | |
| | 0.336 | 2100 | 0.4971 | 0.4141 | 0.9028 | |
| | 0.352 | 2200 | 0.4969 | 0.4229 | 0.8999 | |
| | 0.368 | 2300 | 0.4824 | 0.4106 | 0.9039 | |
| | 0.384 | 2400 | 0.4854 | 0.4117 | 0.8952 | |
| | 0.4 | 2500 | 0.4874 | 0.4071 | 0.9019 | |
| | 0.416 | 2600 | 0.4675 | 0.4428 | 0.8981 | |
| | 0.432 | 2700 | 0.517 | 0.4130 | 0.9019 | |
| | 0.448 | 2800 | 0.4514 | 0.4361 | 0.9034 | |
| | 0.464 | 2900 | 0.4981 | 0.3958 | 0.9077 | |
| | 0.48 | 3000 | 0.4461 | 0.4124 | 0.9066 | |
| | 0.496 | 3100 | 0.4662 | 0.4147 | 0.9066 | |
| | 0.512 | 3200 | 0.3938 | 0.4122 | 0.9020 | |
| | 0.528 | 3300 | 0.4122 | 0.4157 | 0.9001 | |
| | 0.544 | 3400 | 0.4387 | 0.4118 | 0.9029 | |
| | 0.56 | 3500 | 0.4181 | 0.3876 | 0.9042 | |
| | 0.576 | 3600 | 0.3603 | 0.3888 | 0.9048 | |
| | 0.592 | 3700 | 0.4182 | 0.3936 | 0.8988 | |
| | 0.608 | 3800 | 0.3918 | 0.3996 | 0.9013 | |
| | 0.624 | 3900 | 0.4158 | 0.3777 | 0.9074 | |
| | 0.64 | 4000 | 0.3861 | 0.3689 | 0.9081 | |
| | 0.656 | 4100 | 0.3142 | 0.3842 | 0.9086 | |
| | 0.672 | 4200 | 0.3327 | 0.3794 | 0.9090 | |
| | 0.688 | 4300 | 0.3784 | 0.3785 | 0.9052 | |
| | 0.704 | 4400 | 0.3208 | 0.3849 | 0.9017 | |
| | 0.72 | 4500 | 0.3591 | 0.3910 | 0.9070 | |
| | 0.736 | 4600 | 0.3331 | 0.3817 | 0.9092 | |
| | 0.752 | 4700 | 0.3567 | 0.3762 | 0.9125 | |
| | 0.768 | 4800 | 0.3445 | 0.3639 | 0.9108 | |
| | 0.784 | 4900 | 0.3472 | 0.3723 | 0.9116 | |
| | 0.8 | 5000 | 0.2895 | 0.3685 | 0.9115 | |
| | 0.816 | 5100 | 0.3067 | 0.3714 | 0.9121 | |
| | 0.832 | 5200 | 0.3139 | 0.3623 | 0.9134 | |
| | 0.848 | 5300 | 0.3106 | 0.3635 | 0.9127 | |
| | 0.864 | 5400 | 0.2965 | 0.3681 | 0.9136 | |
| | 0.88 | 5500 | 0.3154 | 0.3646 | 0.9145 | |
| | 0.896 | 5600 | 0.2963 | 0.3548 | 0.9154 | |
| | 0.912 | 5700 | 0.296 | 0.3550 | 0.9152 | |
| | 0.928 | 5800 | 0.2847 | 0.3535 | 0.9157 | |
| | 0.944 | 5900 | 0.2732 | 0.3522 | 0.9162 | |
| | 0.96 | 6000 | 0.3154 | 0.3502 | 0.9168 | |
| | 0.976 | 6100 | 0.2979 | 0.3510 | 0.9165 | |
| | 0.992 | 6200 | 0.1601 | 0.3502 | 0.9163 | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.11.12 |
| - Sentence Transformers: 4.1.0 |
| - Transformers: 4.51.3 |
| - PyTorch: 2.6.0+cu124 |
| - Accelerate: 1.6.0 |
| - Datasets: 3.6.0 |
| - Tokenizers: 0.21.1 |
|
|
| ## 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} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
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| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
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| ## Model Card Contact |
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| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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