README.md

---
base_model: google-bert/bert-base-uncased
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:103663
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How much native Icelandic and advanced Icelandic learners can read
    and understand Old Norse?
  sentences:
  - What are the best answers for "Why should I hire you?"in a cool way?
  - Are girls shy in expressing their feelings?
  - If I learn Icelandic can I understand old norse texts?
- source_sentence: Where can I get quality assistance for budget conveyancing across
    the Sydney?
  sentences:
  - What are the possible options for India to deal with Uri terror attack?
  - What is the intended purpose of philosophy?
  - Where can I get quality assistance in Sydney for any property transaction?
- source_sentence: What are some of the best IAS coaching institutions in Mumbai?
  sentences:
  - What are best IAS coaching institutes in Mumbai?
  - Do vampires really exist?
  - What do most women feel during sex?
- source_sentence: Is petroleum engineering still a good major?
  sentences:
  - What are some of the best sex stories?
  - Can I clear CAT in 4.5 months?
  - What is the future of petroleum engineering graduating in 2020?
- source_sentence: How can the drive from Edmonton to Auckland be described, and how
    do these cities' attractions compare to those in Vancouver?
  sentences:
  - How can the drive from Edmonton to Auckland be described, and how does the history
    of these cities compare and contrast to the history of Vancouver?
  - What are the best hashtags to use as a photographer on instagram?
  - Which optional subjects can I choose for the IAS exam?
model-index:
- name: SentenceTransformer based on google-bert/bert-base-uncased
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.7643828947012523
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.8147265911102295
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.6959193470955354
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7402496337890625
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.5945532101060921
      name: Cosine Precision
    - type: cosine_recall
      value: 0.838953622964735
      name: Cosine Recall
    - type: cosine_ap
      value: 0.7112611713824615
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.7399583457304374
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 153.5009765625
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.6710917251406536
      name: Dot F1
    - type: dot_f1_threshold
      value: 133.23265075683594
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.5683387761657477
      name: Dot Precision
    - type: dot_recall
      value: 0.8191990122694652
      name: Dot Recall
    - type: dot_ap
      value: 0.6542447011722929
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.7665197046333613
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 176.4288787841797
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.6972882533068157
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 218.96762084960938
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.590020301314243
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.8522262520256193
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.7109056366977289
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.7665197046333613
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 8.092199325561523
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.6970045347129081
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 9.794208526611328
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.5945518932171071
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.8421174473338993
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.7109417385930392
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.7665197046333613
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 176.4288787841797
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.6972882533068157
      name: Max F1
    - type: max_f1_threshold
      value: 218.96762084960938
      name: Max F1 Threshold
    - type: max_precision
      value: 0.5945532101060921
      name: Max Precision
    - type: max_recall
      value: 0.8522262520256193
      name: Max Recall
    - type: max_ap
      value: 0.7112611713824615
      name: Max Ap
---

# 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:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **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': 128, '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("gavinqiangli/my-awesome-bi-encoder")
# Run inference
sentences = [
    "How can the drive from Edmonton to Auckland be described, and how do these cities' attractions compare to those in Vancouver?",
    'How can the drive from Edmonton to Auckland be described, and how does the history of these cities compare and contrast to the history of Vancouver?',
    'Which optional subjects can I choose for the IAS exam?',
]
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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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Evaluation

### Metrics

#### Binary Classification

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

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.7644     |
| cosine_accuracy_threshold    | 0.8147     |
| cosine_f1                    | 0.6959     |
| cosine_f1_threshold          | 0.7402     |
| cosine_precision             | 0.5946     |
| cosine_recall                | 0.839      |
| cosine_ap                    | 0.7113     |
| dot_accuracy                 | 0.74       |
| dot_accuracy_threshold       | 153.501    |
| dot_f1                       | 0.6711     |
| dot_f1_threshold             | 133.2327   |
| dot_precision                | 0.5683     |
| dot_recall                   | 0.8192     |
| dot_ap                       | 0.6542     |
| manhattan_accuracy           | 0.7665     |
| manhattan_accuracy_threshold | 176.4289   |
| manhattan_f1                 | 0.6973     |
| manhattan_f1_threshold       | 218.9676   |
| manhattan_precision          | 0.59       |
| manhattan_recall             | 0.8522     |
| manhattan_ap                 | 0.7109     |
| euclidean_accuracy           | 0.7665     |
| euclidean_accuracy_threshold | 8.0922     |
| euclidean_f1                 | 0.697      |
| euclidean_f1_threshold       | 9.7942     |
| euclidean_precision          | 0.5946     |
| euclidean_recall             | 0.8421     |
| euclidean_ap                 | 0.7109     |
| max_accuracy                 | 0.7665     |
| max_accuracy_threshold       | 176.4289   |
| max_f1                       | 0.6973     |
| max_f1_threshold             | 218.9676   |
| max_precision                | 0.5946     |
| max_recall                   | 0.8522     |
| **max_ap**                   | **0.7113** |

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### Recommendations

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 103,663 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        | label                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 13.82 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.87 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>0: ~4.80%</li><li>1: ~95.20%</li></ul> |
* Samples:
  | sentence_0                                                                           | sentence_1                                               | label          |
  |:-------------------------------------------------------------------------------------|:---------------------------------------------------------|:---------------|
  | <code>Are Jewish people the most intelligent in the universe?</code>                 | <code>Why are Jewish people so intelligent?</code>       | <code>1</code> |
  | <code>How do I become a good lawyer? What are the qualities of a good lawyer?</code> | <code>How can someone become a successful lawyer?</code> | <code>1</code> |
  | <code>Why is China going to the Moon?</code>                                         | <code>What does China want with the moon?</code>         | <code>1</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
- `multi_dataset_batch_sampler`: round_robin

#### 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
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: 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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss | max_ap |
|:------:|:----:|:-------------:|:------:|
| 0.0772 | 500  | 0.0796        | -      |
| 0.1543 | 1000 | 0.0205        | 0.6878 |
| 0.2315 | 1500 | 0.0197        | -      |
| 0.3087 | 2000 | 0.0201        | 0.6864 |
| 0.3859 | 2500 | 0.0185        | -      |
| 0.4630 | 3000 | 0.0161        | 0.6933 |
| 0.5402 | 3500 | 0.0163        | -      |
| 0.6174 | 4000 | 0.0172        | 0.7089 |
| 0.6946 | 4500 | 0.0172        | -      |
| 0.7717 | 5000 | 0.0143        | 0.7072 |
| 0.8489 | 5500 | 0.0129        | -      |
| 0.9261 | 6000 | 0.0124        | 0.7112 |
| 1.0    | 6479 | -             | 0.7113 |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.1.0
- Tokenizers: 0.19.1

## 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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