README.md

---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:101762
- loss:TripletLoss
base_model: google-bert/bert-base-uncased
widget:
- source_sentence: Why am I still afraid of the dark?
  sentences:
  - What one single change can change a life?
  - Why do we have a dark side?
  - Why are humans afraid of the dark?
- source_sentence: How did you feel when you had sex for the first time?
  sentences:
  - What do you mean by hypocrite?
  - What is the feeling to have sexual intercourse at the first time?
  - What does receiving anal sex for the first time feel like?
- source_sentence: How much sleep do we really need as an adult in a night?
  sentences:
  - What does histrionic personality disorder feel like physically to you?
  - How much hours should we sleep daily?
  - How do you sleep 7 hours a day?
- source_sentence: How can I learn English from the beginning?
  sentences:
  - Why am I learning English?
  - How do you post a question on Quora?
  - How do I learn English?
- source_sentence: How can I open my computer if I forget my password?
  sentences:
  - What's my state Id number?
  - I forgot my security code on my Nokia 206 how can I unlock it?
  - I forget my PC password what should I do to open it?
datasets:
- embedding-data/QQP_triplets
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on google-bert/bert-base-uncased
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: sentest
      type: sentest
    metrics:
    - type: cosine_accuracy
      value: 0.9882572889328003
      name: Cosine Accuracy
---

# 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 [qqp_triplets](https://huggingface.co/datasets/embedding-data/QQP_triplets) 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 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [qqp_triplets](https://huggingface.co/datasets/embedding-data/QQP_triplets)
- **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("palusi/sentest")
# Run inference
sentences = [
    'How can I open my computer if I forget my password?',
    'I forget my PC password what should I do to open it?',
    'I forgot my security code on my Nokia 206 how can I unlock it?',
]
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: `sentest`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9883** |

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## Training Details

### Training Dataset

#### qqp_triplets

* Dataset: [qqp_triplets](https://huggingface.co/datasets/embedding-data/QQP_triplets) at [f475d9c](https://huggingface.co/datasets/embedding-data/QQP_triplets/tree/f475d9ca10f6eae1f39e756d14610ce7c5bb515c)
* Size: 101,762 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: 6 tokens</li><li>mean: 13.96 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.99 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.49 tokens</li><li>max: 73 tokens</li></ul> |
* Samples:
  | anchor                                                         | positive                                               | negative                                                      |
  |:---------------------------------------------------------------|:-------------------------------------------------------|:--------------------------------------------------------------|
  | <code>Who are Mona Punjabi?</code>                             | <code>Who are Mona punjabis?</code>                    | <code>Why are Punjabis so proud of their Punjabi-hood?</code> |
  | <code>What are some of the best books on/by Bill Gates?</code> | <code>What are the best books of Bill Gates?</code>    | <code>Are there any films about Bill Gates?</code>            |
  | <code>Where can I get best pasta in Bangalore?</code>          | <code>Where can I get best pasta in Bangalore ?</code> | <code>Where can I get best street food in Bangalore?</code>   |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

### Evaluation Dataset

#### qqp_triplets

* Dataset: [qqp_triplets](https://huggingface.co/datasets/embedding-data/QQP_triplets) at [f475d9c](https://huggingface.co/datasets/embedding-data/QQP_triplets/tree/f475d9ca10f6eae1f39e756d14610ce7c5bb515c)
* Size: 101,762 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: 13.99 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.76 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.75 tokens</li><li>max: 78 tokens</li></ul> |
* Samples:
  | anchor                                                     | positive                                                                                   | negative                                                                                                             |
  |:-----------------------------------------------------------|:-------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------|
  | <code>How do l study efficiently?</code>                   | <code>How do you study effectively?</code>                                                 | <code>Why can't I study efficiently?</code>                                                                          |
  | <code>How do you commit suicide?</code>                    | <code>What is the easiest way to commite suicide?</code>                                   | <code>What is a way to commit suicide and not damaging your organs so that they can be donated?</code>               |
  | <code>How do you learn to speak a foreign language?</code> | <code>What is the quickest way a person can learn to speak a new language fluently?</code> | <code>What's the easiest foreign language for a native English speaker, living in America, to learn to speak?</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `push_to_hub`: True
- `hub_model_id`: palusi/sentest
- `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`: 0.01
- `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`: True
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: palusi/sentest
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step     | Training Loss | Validation Loss | sentest_cosine_accuracy |
|:----------:|:--------:|:-------------:|:---------------:|:-----------------------:|
| -1         | -1       | -             | -               | 0.8806                  |
| 0.0983     | 500      | 2.5691        | -               | -                       |
| 0.1965     | 1000     | 1.2284        | 0.6712          | 0.9645                  |
| 0.2948     | 1500     | 0.8769        | -               | -                       |
| 0.3930     | 2000     | 0.7151        | 0.4490          | 0.9787                  |
| 0.4913     | 2500     | 0.6506        | -               | -                       |
| 0.5895     | 3000     | 0.5855        | 0.3519          | 0.9848                  |
| 0.6878     | 3500     | 0.5397        | -               | -                       |
| 0.7860     | 4000     | 0.4998        | 0.3079          | 0.9871                  |
| 0.8843     | 4500     | 0.4885        | -               | -                       |
| **0.9825** | **5000** | **0.483**     | **0.288**       | **0.9883**              |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.2
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- 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",
}
```

#### TripletLoss
```bibtex
@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

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