sentence-transformers safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:557850 loss:MultipleNegativesRankingLoss en dataset:sentence-transformers/all-nli arxiv:1908.10084 arxiv:1705.00652 base_model:microsoft/mpnet-base base_model:finetune:microsoft/mpnet-base license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
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: 0.9155528554070473
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.08475091130012151
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.912363304981774
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9113001215066828
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9155528554070473
      name: Max Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: all nli test
      type: all-nli-test
    metrics:
    - type: cosine_accuracy
      value: 0.9261612952035103
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.07262823422605538
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.9196550158874263
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9201089423513391
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9261612952035103
      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) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **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("bingcheng9/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]
```

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## 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    | 0.9156     |
| dot_accuracy       | 0.0848     |
| manhattan_accuracy | 0.9124     |
| euclidean_accuracy | 0.9113     |
| **max_accuracy**   | **0.9156** |

#### Triplet
* Dataset: `all-nli-test`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy    | 0.9262     |
| dot_accuracy       | 0.0726     |
| manhattan_accuracy | 0.9197     |
| euclidean_accuracy | 0.9201     |
| **max_accuracy**   | **0.9262** |

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## 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: <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
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `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`: 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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### 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        | 1.5784 | 0.7751                   | -                         |
| 0.032 | 200  | 1.2537        | 0.9115 | 0.7983                   | -                         |
| 0.048 | 300  | 1.435         | 0.7883 | 0.8095                   | -                         |
| 0.064 | 400  | 0.8952        | 0.7637 | 0.8112                   | -                         |
| 0.08  | 500  | 0.8482        | 0.8154 | 0.8086                   | -                         |
| 0.096 | 600  | 1.056         | 0.8993 | 0.8033                   | -                         |
| 0.112 | 700  | 0.967         | 0.8740 | 0.8007                   | -                         |
| 0.128 | 800  | 1.1139        | 1.0261 | 0.7930                   | -                         |
| 0.144 | 900  | 1.1765        | 0.9142 | 0.8127                   | -                         |
| 0.16  | 1000 | 1.1022        | 0.8580 | 0.7980                   | -                         |
| 0.176 | 1100 | 1.1095        | 1.0273 | 0.7889                   | -                         |
| 0.192 | 1200 | 1.0725        | 0.9443 | 0.7998                   | -                         |
| 0.208 | 1300 | 0.9075        | 0.8191 | 0.8070                   | -                         |
| 0.224 | 1400 | 0.7504        | 0.8069 | 0.8104                   | -                         |
| 0.24  | 1500 | 0.815         | 0.7824 | 0.8193                   | -                         |
| 0.256 | 1600 | 0.6089        | 0.8256 | 0.8168                   | -                         |
| 0.272 | 1700 | 0.8689        | 0.8470 | 0.8079                   | -                         |
| 0.288 | 1800 | 0.8359        | 0.8588 | 0.8103                   | -                         |
| 0.304 | 1900 | 0.8157        | 0.7955 | 0.8129                   | -                         |
| 0.32  | 2000 | 0.7511        | 0.7027 | 0.8467                   | -                         |
| 0.336 | 2100 | 0.603         | 0.7624 | 0.8467                   | -                         |
| 0.352 | 2200 | 0.6005        | 0.7071 | 0.8686                   | -                         |
| 0.368 | 2300 | 0.8079        | 0.7497 | 0.8492                   | -                         |
| 0.384 | 2400 | 0.7237        | 0.6801 | 0.8586                   | -                         |
| 0.4   | 2500 | 0.669         | 0.6595 | 0.8694                   | -                         |
| 0.416 | 2600 | 0.6013        | 0.6700 | 0.8587                   | -                         |
| 0.432 | 2700 | 0.8929        | 0.7217 | 0.8645                   | -                         |
| 0.448 | 2800 | 0.8627        | 0.6720 | 0.8521                   | -                         |
| 0.464 | 2900 | 0.8279        | 0.6561 | 0.8698                   | -                         |
| 0.48  | 3000 | 0.6893        | 0.6243 | 0.8692                   | -                         |
| 0.496 | 3100 | 0.7609        | 0.6052 | 0.8711                   | -                         |
| 0.512 | 3200 | 0.5704        | 0.6042 | 0.8677                   | -                         |
| 0.528 | 3300 | 0.6117        | 0.5398 | 0.8827                   | -                         |
| 0.544 | 3400 | 0.5231        | 0.5743 | 0.8797                   | -                         |
| 0.56  | 3500 | 0.5231        | 0.5817 | 0.8923                   | -                         |
| 0.576 | 3600 | 0.4825        | 0.5309 | 0.8911                   | -                         |
| 0.592 | 3700 | 0.5464        | 0.5261 | 0.8961                   | -                         |
| 0.608 | 3800 | 0.4846        | 0.5017 | 0.8979                   | -                         |
| 0.624 | 3900 | 0.4896        | 0.5280 | 0.8947                   | -                         |
| 0.64  | 4000 | 0.7499        | 0.5435 | 0.9061                   | -                         |
| 0.656 | 4100 | 0.916         | 0.5268 | 0.9060                   | -                         |
| 0.672 | 4200 | 0.8733        | 0.4855 | 0.9074                   | -                         |
| 0.688 | 4300 | 0.6963        | 0.4717 | 0.9105                   | -                         |
| 0.704 | 4400 | 0.5907        | 0.4567 | 0.9142                   | -                         |
| 0.72  | 4500 | 0.5768        | 0.4702 | 0.9111                   | -                         |
| 0.736 | 4600 | 0.6173        | 0.4491 | 0.9151                   | -                         |
| 0.752 | 4700 | 0.6802        | 0.4680 | 0.9124                   | -                         |
| 0.768 | 4800 | 0.6099        | 0.4372 | 0.9130                   | -                         |
| 0.784 | 4900 | 0.5689        | 0.4480 | 0.9066                   | -                         |
| 0.8   | 5000 | 0.6554        | 0.4603 | 0.9118                   | -                         |
| 0.816 | 5100 | 0.511         | 0.4356 | 0.9116                   | -                         |
| 0.832 | 5200 | 0.5725        | 0.4246 | 0.9092                   | -                         |
| 0.848 | 5300 | 0.5196        | 0.4359 | 0.9107                   | -                         |
| 0.864 | 5400 | 0.6112        | 0.4403 | 0.9104                   | -                         |
| 0.88  | 5500 | 0.5233        | 0.4236 | 0.9115                   | -                         |
| 0.896 | 5600 | 0.5467        | 0.4217 | 0.9127                   | -                         |
| 0.912 | 5700 | 0.6109        | 0.4199 | 0.9156                   | -                         |
| 0.928 | 5800 | 0.54          | 0.4077 | 0.9148                   | -                         |
| 0.944 | 5900 | 0.6739        | 0.4111 | 0.9145                   | -                         |
| 0.96  | 6000 | 0.723         | 0.4170 | 0.9154                   | -                         |
| 0.976 | 6100 | 0.6753        | 0.4162 | 0.9154                   | -                         |
| 0.992 | 6200 | 0.0591        | 0.4157 | 0.9156                   | -                         |
| 1.0   | 6250 | -             | -      | -                        | 0.9262                    |


### Framework Versions
- Python: 3.12.4
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.2.2
- Accelerate: 0.26.0
- Datasets: 3.0.2
- Tokenizers: 0.20.1

## 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}
}
```

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