sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:494430 loss:SoftmaxLoss en dataset:stanfordnlp/snli arxiv:1908.10084 base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: google-bert/bert-base-uncased
datasets:
- stanfordnlp/snli
language:
- en
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:494430
- loss:SoftmaxLoss
widget:
- source_sentence: A person out front of a business with a woman statue holding a
    bottle.
  sentences:
  - A couple holds hands.
  - The young boy is upside down.
  - the man is baking some bread
- source_sentence: A person is dressed up in a weird costume with a red tongue sticking
    out.
  sentences:
  - thhe man plays a tuba
  - Four siblings are climbing on a fake black bear.
  - the tongue is blue
- source_sentence: A man on a train is talking on a cellphone.
  sentences:
  - A man is playing a flute on a bus.
  - The woman is sexy.
  - two cyclists racing
- source_sentence: An elderly woman giving her daughter a hug.
  sentences:
  - There are two women hugging.
  - A man holds a flag on the street.
  - people are sitting on a red roofed bus going to a museum
- source_sentence: A pilot dressed in a dark-colored sweater is sitting in the cock-pit
    of a plane with his hands crossed.
  sentences:
  - A pilot is sitting in his plain with his hands crossed
  - The boys are playing outside on a log.
  - Two men discuss their love lives.
---

# SentenceTransformer based on google-bert/bert-base-uncased

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) 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:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli)
- **Language:** en
<!-- - **License:** Unknown -->

### 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: BertModel 
  (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("hcy5561/distilroberta-base-sentence-transformer-snli")
# Run inference
sentences = [
    'A pilot dressed in a dark-colored sweater is sitting in the cock-pit of a plane with his hands crossed.',
    'A pilot is sitting in his plain with his hands crossed',
    'The boys are playing outside on a log.',
]
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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## Training Details

### Training Dataset

#### stanfordnlp/snli

* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 494,430 training samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | premise                                                                           | hypothesis                                                                        | label                                                              |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                                                |
  | details | <ul><li>min: 6 tokens</li><li>mean: 16.24 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.55 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>0: ~31.10%</li><li>1: ~33.40%</li><li>2: ~35.50%</li></ul> |
* Samples:
  | premise                                                                       | hypothesis                             | label          |
  |:------------------------------------------------------------------------------|:---------------------------------------|:---------------|
  | <code>Two men, one in yellow, are on a wooden boat.</code>                    | <code>Two men swimming in water</code> | <code>2</code> |
  | <code>Two people sleep on a couch.</code>                                     | <code>Two people are asleep.</code>    | <code>0</code> |
  | <code>a little boy is learning to swim with the help of a float board.</code> | <code>The boy is crawling.</code>      | <code>2</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

### Evaluation Dataset

#### stanfordnlp/snli

* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 27,468 evaluation samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | premise                                                                           | hypothesis                                                                        | label                                                              |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                                                |
  | details | <ul><li>min: 6 tokens</li><li>mean: 16.66 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.48 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>0: ~36.10%</li><li>1: ~31.80%</li><li>2: ~32.10%</li></ul> |
* Samples:
  | premise                                                                                                  | hypothesis                                                                                                  | label          |
  |:---------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>A taxi cab driver looks stressed out in his car.</code>                                            | <code>a taxi driver is stressed</code>                                                                      | <code>0</code> |
  | <code>Two men do trick in a park.</code>                                                                 | <code>The men only sat on the bench in the park, doing nothing.</code>                                      | <code>2</code> |
  | <code>Two woman walking, the blond is looking at the camera wearing sunglasses making an oh face.</code> | <code>One lady makes a shocked face at the camera as the photographer tells the women they are lost.</code> | <code>1</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_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`: 4
- `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
- `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}
- `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
- `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_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step  | Training Loss | loss   |
|:------:|:-----:|:-------------:|:------:|
| 0.1294 | 1000  | 0.9208        | 0.7448 |
| 0.2589 | 2000  | 0.7095        | 0.6462 |
| 0.3883 | 3000  | 0.6415        | 0.6199 |
| 0.5177 | 4000  | 0.6125        | 0.5940 |
| 0.6472 | 5000  | 0.5935        | 0.5672 |
| 0.7766 | 6000  | 0.5748        | 0.5550 |
| 0.9060 | 7000  | 0.5654        | 0.5506 |
| 1.0355 | 8000  | 0.5524        | 0.5376 |
| 1.1649 | 9000  | 0.5386        | 0.5319 |
| 1.2943 | 10000 | 0.5192        | 0.5361 |
| 1.4238 | 11000 | 0.4863        | 0.5304 |
| 1.5532 | 12000 | 0.4687        | 0.5278 |
| 1.6826 | 13000 | 0.4586        | 0.5305 |
| 1.8121 | 14000 | 0.4474        | 0.5222 |
| 1.9415 | 15000 | 0.4447        | 0.5237 |
| 2.0709 | 16000 | 0.434         | 0.5172 |
| 2.2004 | 17000 | 0.4243        | 0.5235 |
| 2.3298 | 18000 | 0.398         | 0.5224 |
| 2.4592 | 19000 | 0.3747        | 0.5344 |
| 2.5887 | 20000 | 0.3669        | 0.5301 |
| 2.7181 | 21000 | 0.3583        | 0.5406 |
| 2.8475 | 22000 | 0.3496        | 0.5354 |
| 2.9770 | 23000 | 0.3527        | 0.5324 |
| 3.1064 | 24000 | 0.3419        | 0.5299 |
| 3.2358 | 25000 | 0.3358        | 0.5456 |
| 3.3653 | 26000 | 0.3096        | 0.5562 |
| 3.4947 | 27000 | 0.2964        | 0.5644 |
| 3.6241 | 28000 | 0.2998        | 0.5565 |
| 3.7536 | 29000 | 0.2906        | 0.5590 |
| 3.8830 | 30000 | 0.2923        | 0.5564 |


### Framework Versions
- Python: 3.10.6
- Sentence Transformers: 3.0.1
- Transformers: 4.39.3
- PyTorch: 2.2.2+cu118
- Accelerate: 0.28.0
- Datasets: 2.20.0
- Tokenizers: 0.15.2

## Citation

### BibTeX

#### Sentence Transformers and SoftmaxLoss
```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",
}
```

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