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mpnet-base-all-nli-triplet

Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
Generated from Trainer
dataset_size:100000
loss:MultipleNegativesRankingLoss
Eval Results
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mpnet-base-all-nli-triplet / README.md
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 ---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:100000
- 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.
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: 0.9123632907867432
name: Cosine Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: all nli test
type: all-nli-test
metrics:
- type: cosine_accuracy
value: 0.9238916635513306
name: Cosine Accuracy
---
# MPNet base trained on AllNLI triplets
This is a [sentence-transformers](https://www.SBERT.net) model trained on the 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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- 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("gongysh2004/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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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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## Evaluation
### Metrics
#### Triplet
* Datasets: `all-nli-dev` and `all-nli-test`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | all-nli-dev | all-nli-test |
|:--------------------|:------------|:-------------|
| **cosine_accuracy** | **0.9124** | **0.9239** |
<!--
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## Training Details
### Training Dataset
#### all-nli
* Dataset: all-nli
* 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
* 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}
- `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 | all-nli-test_cosine_accuracy |
|:-----:|:----:|:-------------:|:---------------:|:---------------------------:|:----------------------------:|
| -1 | -1 | - | - | 0.6211 | - |
| 0.016 | 100 | 2.5621 | 0.8215 | 0.7918 | - |
| 0.032 | 200 | 1.1921 | 0.5966 | 0.8314 | - |
| 0.048 | 300 | 0.9665 | 0.5667 | 0.8379 | - |
| 0.064 | 400 | 0.9082 | 0.5386 | 0.8489 | - |
| 0.08 | 500 | 0.8789 | 0.4822 | 0.8733 | - |
| 0.096 | 600 | 0.8698 | 0.4591 | 0.8844 | - |
| 0.112 | 700 | 0.8216 | 0.5016 | 0.8826 | - |
| 0.128 | 800 | 0.8062 | 0.5487 | 0.8820 | - |
| 0.144 | 900 | 0.7559 | 0.5344 | 0.8829 | - |
| 0.16 | 1000 | 0.672 | 0.5436 | 0.8714 | - |
| 0.176 | 1100 | 0.715 | 0.4816 | 0.8820 | - |
| 0.192 | 1200 | 0.6801 | 0.4801 | 0.8923 | - |
| 0.208 | 1300 | 0.6512 | 0.4848 | 0.8976 | - |
| 0.224 | 1400 | 0.6277 | 0.4698 | 0.8903 | - |
| 0.24 | 1500 | 0.6759 | 0.4804 | 0.8884 | - |
| 0.256 | 1600 | 0.6087 | 0.4260 | 0.9005 | - |
| 0.272 | 1700 | 0.6139 | 0.4445 | 0.8837 | - |
| 0.288 | 1800 | 0.5811 | 0.4439 | 0.8975 | - |
| 0.304 | 1900 | 0.504 | 0.4258 | 0.9017 | - |
| 0.32 | 2000 | 0.5033 | 0.4708 | 0.9013 | - |
| 0.336 | 2100 | 0.5079 | 0.4197 | 0.9020 | - |
| 0.352 | 2200 | 0.4983 | 0.4201 | 0.9028 | - |
| 0.368 | 2300 | 0.489 | 0.4453 | 0.9007 | - |
| 0.384 | 2400 | 0.4971 | 0.4311 | 0.8969 | - |
| 0.4 | 2500 | 0.4914 | 0.4363 | 0.8947 | - |
| 0.416 | 2600 | 0.4912 | 0.4385 | 0.8979 | - |
| 0.432 | 2700 | 0.5383 | 0.4317 | 0.8958 | - |
| 0.448 | 2800 | 0.4387 | 0.4080 | 0.9033 | - |
| 0.464 | 2900 | 0.4843 | 0.4177 | 0.9017 | - |
| 0.48 | 3000 | 0.4624 | 0.3976 | 0.9040 | - |
| 0.496 | 3100 | 0.4672 | 0.3934 | 0.9069 | - |
| 0.512 | 3200 | 0.4037 | 0.4099 | 0.9031 | - |
| 0.528 | 3300 | 0.4281 | 0.4070 | 0.9011 | - |
| 0.544 | 3400 | 0.4489 | 0.4059 | 0.9067 | - |
| 0.56 | 3500 | 0.4163 | 0.3916 | 0.9055 | - |
| 0.576 | 3600 | 0.3578 | 0.4016 | 0.9058 | - |
| 0.592 | 3700 | 0.4192 | 0.3895 | 0.9037 | - |
| 0.608 | 3800 | 0.3843 | 0.4166 | 0.9072 | - |
| 0.624 | 3900 | 0.387 | 0.3931 | 0.9107 | - |
| 0.64 | 4000 | 0.3924 | 0.3696 | 0.9128 | - |
| 0.656 | 4100 | 0.3209 | 0.3900 | 0.9052 | - |
| 0.672 | 4200 | 0.3363 | 0.3792 | 0.9055 | - |
| 0.688 | 4300 | 0.3834 | 0.3784 | 0.9045 | - |
| 0.704 | 4400 | 0.3218 | 0.3968 | 0.8975 | - |
| 0.72 | 4500 | 0.3702 | 0.4029 | 0.9036 | - |
| 0.736 | 4600 | 0.3243 | 0.3770 | 0.9084 | - |
| 0.752 | 4700 | 0.3423 | 0.3785 | 0.9081 | - |
| 0.768 | 4800 | 0.3606 | 0.3742 | 0.9092 | - |
| 0.784 | 4900 | 0.3709 | 0.3736 | 0.9095 | - |
| 0.8 | 5000 | 0.3014 | 0.3787 | 0.9078 | - |
| 0.816 | 5100 | 0.3205 | 0.3783 | 0.9064 | - |
| 0.832 | 5200 | 0.311 | 0.3753 | 0.9098 | - |
| 0.848 | 5300 | 0.315 | 0.3712 | 0.9111 | - |
| 0.864 | 5400 | 0.3096 | 0.3784 | 0.9127 | - |
| 0.88 | 5500 | 0.3247 | 0.3691 | 0.9116 | - |
| 0.896 | 5600 | 0.3055 | 0.3635 | 0.9124 | - |
| 0.912 | 5700 | 0.3209 | 0.3644 | 0.9128 | - |
| 0.928 | 5800 | 0.2887 | 0.3619 | 0.9113 | - |
| 0.944 | 5900 | 0.2786 | 0.3608 | 0.9119 | - |
| 0.96 | 6000 | 0.3119 | 0.3583 | 0.9119 | - |
| 0.976 | 6100 | 0.3048 | 0.3590 | 0.9122 | - |
| 0.992 | 6200 | 0.1677 | 0.3578 | 0.9124 | - |
| -1 | -1 | - | - | - | 0.9239 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.7.1+cu126
- Accelerate: 1.7.0
- 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}
}
```
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