README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:50000
- loss:CosineSimilarityLoss
base_model: google-bert/bert-base-uncased
widget:
- source_sentence: Sometimes the people who represent themselves don't even know the
    significant facts of their case.
  sentences:
  - The law is very easy to understand, so representing yourself in court is the best
    way to win a case.
  - Sewage poured into upstairs windows from the streets while people whispered to
    each other.
  - His faith may be lacking.
- source_sentence: When he married in 1901, he and his wife (Olga Knipper of the Moscow
    Art Theater) went directly from the ceremony to a honeymoon in a sanitarium.
  sentences:
  - if a person wants to eat you understand that
  - 'His wife has never went to a sanitarium. '
  - The new system appears far more complex, but ultimately easier and more thorough.
- source_sentence: it really is i heard something that their supposed to be starting
    a huge campaign in New York about um child abuse and stopping child abuse and
    it's supposed to be like it's starting there supposed to be like a big nationwide
    campaign and you know so hopefully that will take off and really do something
    i don't know there's just
  sentences:
  - The Washington Post was the first company to report on attempts of private companies
    growing embryos.
  - Me too?
  - It's unfortunate that nobody is organizing a child abuse campaign.
- source_sentence: On the mainland, an invasion of even greater significance followed
    in 1580, when Philip II of Spain proclaimed himself king of Portugal and marched
    his armies across the border.
  sentences:
  - Some of the modern buildings that were erected in their place are not admired
    today.
  - Jon wanted to save them from the angry mob.
  - Philip II of Spain invaded Portugal.
- source_sentence: The river plays a central role in all visits to Paris.
  sentences:
  - He said Dave Hanson.
  - The river is central to all vacations to Paris.
  - Trauma is the leading cause of alcohol abuse.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on google-bert/bert-base-uncased
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: pearson_cosine
      value: 0.7301988757371918
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7323168725786805
      name: Spearman Cosine
---

# 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). 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 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **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("ryanhoangt/bert-base-uncased-mnli-cosine")
# Run inference
sentences = [
    'The river plays a central role in all visits to Paris.',
    'The river is central to all vacations to Paris.',
    'Trauma is the leading cause of alcohol abuse.',
]
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

#### Semantic Similarity

* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7302     |
| **spearman_cosine** | **0.7323** |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 50,000 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                         | label                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | float                                                          |
  | details | <ul><li>min: 4 tokens</li><li>mean: 26.95 tokens</li><li>max: 189 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.11 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.34</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                                                                                                                            | sentence2                                                                         | label            |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------|
  | <code>Conceptually cream skimming has two basic dimensions - product and geography.</code>                                                                                                                                                                                                           | <code>Product and geography are what make cream skimming work. </code>            | <code>0.0</code> |
  | <code>you know during the season and i guess at at your level uh you lose them to the next level if if they decide to recall the the parent team the Braves decide to call to recall a guy from triple A then a double A guy goes up to replace him and a single A guy goes up to replace him</code> | <code>You lose the things to the following level if the people recall.</code>     | <code>1.0</code> |
  | <code>One of our number will carry out your instructions minutely.</code>                                                                                                                                                                                                                            | <code>A member of my team will execute your orders with immense precision.</code> | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

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

- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 1
- `warmup_steps`: 100
- `fp16`: True

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `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`: 0.0
- `warmup_steps`: 100
- `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`: 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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|
| 0.0320 | 50   | 0.2752        | -               |
| 0.0640 | 100  | 0.1898        | -               |
| 0.0960 | 150  | 0.1733        | -               |
| 0.1280 | 200  | 0.1679        | -               |
| 0.1599 | 250  | 0.1743        | -               |
| 0.1919 | 300  | 0.1703        | -               |
| 0.2239 | 350  | 0.1599        | -               |
| 0.2559 | 400  | 0.1614        | -               |
| 0.2879 | 450  | 0.149         | -               |
| 0.3199 | 500  | 0.1555        | -               |
| 0.3519 | 550  | 0.1631        | -               |
| 0.3839 | 600  | 0.1537        | -               |
| 0.4159 | 650  | 0.1497        | -               |
| 0.4479 | 700  | 0.1512        | -               |
| 0.4798 | 750  | 0.157         | -               |
| 0.5118 | 800  | 0.1544        | -               |
| 0.5438 | 850  | 0.1502        | -               |
| 0.5758 | 900  | 0.1459        | -               |
| 0.6078 | 950  | 0.1476        | -               |
| 0.6398 | 1000 | 0.1439        | -               |
| 0.6718 | 1050 | 0.1508        | -               |
| 0.7038 | 1100 | 0.1444        | -               |
| 0.7358 | 1150 | 0.1457        | -               |
| 0.7678 | 1200 | 0.1486        | -               |
| 0.7997 | 1250 | 0.1485        | -               |
| 0.8317 | 1300 | 0.1419        | -               |
| 0.8637 | 1350 | 0.1406        | -               |
| 0.8957 | 1400 | 0.1407        | -               |
| 0.9277 | 1450 | 0.1434        | -               |
| 0.9597 | 1500 | 0.1365        | -               |
| 0.9917 | 1550 | 0.1465        | -               |
| -1     | -1   | -             | 0.7323          |


### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.1.0
- Transformers: 4.52.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 3.2.0
- 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",
}
```

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