README.md

---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:DenoisingAutoEncoderLoss
base_model: google-bert/bert-base-cased
widget:
- source_sentence: A man his
  sentences:
  - A construction worker peeking out of a manhole while his coworker sits on the
    sidewalk smiling.
  - A man is jumping unto his filthy bed.
  - A man is sitting in a chair and looking at something that he is holding.
- source_sentence: A and a woman walking with a a
  sentences:
  - A man and a woman is walking with a dog across a beach
  - A baby smiles while swinging in a blue infant swing.
  - A man uses a projector to give a presentation.
- source_sentence: blue
  sentences:
  - A baby wearing a bib makes a funny face at the camera.
  - The man is wearing a blue shirt.
  - There are three policemen on bikes making sure that the streets are cleared for
    the president.
- source_sentence: Two boys and
  sentences:
  - Two boys sitting and eating ice cream.
  - A man with a hat, boots, and brown pants, is playing the violin outside in front
    of a black structure.
  - A man is a safety suit walking outside while another man in a dark suit walks
    into a building.
- source_sentence: A finds humorous that.
  sentences:
  - A older gentleman finds it humorous that he is getting his picture taken while
    doing his laundry.
  - A dark-skinned man smoking a cigarette near a green trashcan.
  - A woman walks on a sidewalk wearing a white dress with a blue plaid pattern.
datasets:
- sentence-transformers/all-nli
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

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

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) 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:** [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) <!-- at revision cd5ef92a9fb2f889e972770a36d4ed042daf221e -->
- **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:** 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("jinoooooooooo/bert-base-cased-nli-tsdae")
# Run inference
sentences = [
    'A finds humorous that.',
    'A older gentleman finds it humorous that he is getting his picture taken while doing his laundry.',
    'A woman walks on a sidewalk wearing a white dress with a blue plaid pattern.',
]
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

#### 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>damaged</code> and <code>original</code>
* Approximate statistics based on the first 1000 samples:
  |         | damaged                                                                          | original                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 5.45 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 10.49 tokens</li><li>max: 46 tokens</li></ul> |
* Samples:
  | damaged                      | original                                                                   |
  |:-----------------------------|:---------------------------------------------------------------------------|
  | <code>a horse jumps a</code> | <code>A person on a horse jumps over a broken down airplane.</code>        |
  | <code>at</code>              | <code>Children smiling and waving at camera</code>                         |
  | <code>boy jumping a.</code>  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> |
* Loss: [<code>DenoisingAutoEncoderLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss)

### 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>damaged</code> and <code>original</code>
* Approximate statistics based on the first 1000 samples:
  |         | damaged                                                                          | original                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 8.52 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.26 tokens</li><li>max: 69 tokens</li></ul> |
* Samples:
  | damaged                                                                                            | original                                                                                                                                                                       |
  |:---------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Two while packages.</code>                                                                   | <code>Two women are embracing while holding to go packages.</code>                                                                                                             |
  | <code>young children, with the number one with 2 are standing wooden in a bathroom in sink.</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>A a during world city of</code>                                                              | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    |
* Loss: [<code>DenoisingAutoEncoderLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss)

### 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
- `fp16`: True

#### 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`: 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
- `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
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | Training Loss | Validation Loss |
|:-----:|:----:|:-------------:|:---------------:|
| 0.016 | 100  | 7.3226        | 7.2198          |
| 0.032 | 200  | 3.7141        | 6.3506          |
| 0.048 | 300  | 3.0632        | 5.8854          |
| 0.064 | 400  | 2.6549        | 5.7539          |
| 0.08  | 500  | 2.5332        | 5.5007          |
| 0.096 | 600  | 2.3137        | 5.5201          |
| 0.112 | 700  | 2.2533        | 5.3476          |
| 0.128 | 800  | 2.0654        | 5.3438          |
| 0.144 | 900  | 1.9943        | 5.3552          |
| 0.16  | 1000 | 1.9587        | 5.2709          |
| 0.176 | 1100 | 1.8053        | 5.4117          |
| 0.192 | 1200 | 1.7414        | 5.4315          |
| 0.208 | 1300 | 1.6773        | 5.2983          |
| 0.224 | 1400 | 1.6035        | 5.5064          |
| 0.24  | 1500 | 1.5592        | 5.5167          |
| 0.256 | 1600 | 1.5837        | 5.4428          |
| 0.272 | 1700 | 1.469         | 5.5266          |
| 0.288 | 1800 | 1.384         | 5.5159          |
| 0.304 | 1900 | 1.3616        | 5.4305          |
| 0.32  | 2000 | 1.3065        | 5.5076          |
| 0.336 | 2100 | 1.3045        | 5.5460          |
| 0.352 | 2200 | 1.3447        | 5.3051          |
| 0.368 | 2300 | 1.3367        | 5.4867          |
| 0.384 | 2400 | 1.148         | 5.6086          |
| 0.4   | 2500 | 1.2229        | 5.5027          |
| 0.416 | 2600 | 1.16          | 5.4446          |
| 0.432 | 2700 | 1.1809        | 5.4059          |
| 0.448 | 2800 | 1.2099        | 5.6255          |
| 0.464 | 2900 | 1.1264        | 5.2683          |
| 0.48  | 3000 | 1.1589        | 5.3651          |
| 0.496 | 3100 | 1.0954        | 5.3109          |
| 0.512 | 3200 | 1.0962        | 5.4071          |
| 0.528 | 3300 | 1.1185        | 5.4022          |
| 0.544 | 3400 | 1.0656        | 5.2648          |
| 0.56  | 3500 | 1.0935        | 5.2185          |
| 0.576 | 3600 | 1.0235        | 5.2950          |
| 0.592 | 3700 | 1.0256        | 5.3534          |
| 0.608 | 3800 | 0.9711        | 5.2015          |
| 0.624 | 3900 | 0.9901        | 5.1011          |
| 0.64  | 4000 | 0.9959        | 5.2055          |
| 0.656 | 4100 | 1.0018        | 5.2456          |
| 0.672 | 4200 | 0.9836        | 5.3166          |
| 0.688 | 4300 | 1.0481        | 5.2324          |
| 0.704 | 4400 | 0.9917        | 5.1831          |
| 0.72  | 4500 | 0.9595        | 5.1268          |
| 0.736 | 4600 | 1.0096        | 5.1112          |
| 0.752 | 4700 | 0.9986        | 5.0724          |
| 0.768 | 4800 | 0.9405        | 5.1163          |
| 0.784 | 4900 | 0.9057        | 5.0673          |
| 0.8   | 5000 | 0.9938        | 4.9926          |
| 0.816 | 5100 | 0.9849        | 4.9733          |
| 0.832 | 5200 | 0.8973        | 5.0531          |
| 0.848 | 5300 | 0.924         | 5.0007          |
| 0.864 | 5400 | 0.9516        | 5.0079          |
| 0.88  | 5500 | 0.9637        | 4.9513          |
| 0.896 | 5600 | 0.9232        | 5.0035          |
| 0.912 | 5700 | 0.9518        | 4.9339          |
| 0.928 | 5800 | 0.8939        | 4.9783          |
| 0.944 | 5900 | 0.8752        | 4.9495          |
| 0.96  | 6000 | 0.9187        | 4.9496          |
| 0.976 | 6100 | 0.8987        | 4.9177          |
| 0.992 | 6200 | 0.9034        | 4.9224          |


### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.4.0.dev0
- Transformers: 4.47.0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.1.0
- Tokenizers: 0.21.0

## 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",
}
```

#### DenoisingAutoEncoderLoss
```bibtex
@inproceedings{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    pages = "671--688",
    url = "https://arxiv.org/abs/2104.06979",
}
```

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