README.md

---
base_model: distilbert/distilroberta-base
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:50881
- loss:TripletLoss
widget:
- source_sentence: How can we reduce fatty thighs?
  sentences:
  - Is running beneficial for burning thigh and hips fat?
  - Do mosquitoes get trapped in spider webs?
  - How can I reduce thigh fat?
- source_sentence: What does Balaji Vishwanathan think about the ban of ₹500 and ₹1000
    currency notes in India?
  sentences:
  - What are your views on demonetization of ₹500 & ₹1000 notes in India?
  - What is your view on Meenakshi Lekhi, a MP of BJP, suggesting that demonetization
    will hurt the common people?
  - What are some good horror movies?
- source_sentence: What are your New Years resolutions for 2017?
  sentences:
  - What are some meaningful new year resolutions for 2017?
  - How close are we to world war?
  - What are your New Year's resolutions for 2016?
- source_sentence: Which will be the best day of your life?
  sentences:
  - Can you describe the best moment or the best day in your life?
  - How was your day? What did you do today?
  - Is it possible to travel time with real life?
- source_sentence: What is the best way to learn to play piano?
  sentences:
  - How can I learn to play the piano/synthesizer?
  - What are the facilities to an IES officer?
  - Can I easily learn a piano at a later point if I start learning music with a keyboard
    initially?
---

# SentenceTransformer based on distilbert/distilroberta-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base). 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:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (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("AhmedSSoliman/distilroberta-base-sentence-transformer")
# Run inference
sentences = [
    'What is the best way to learn to play piano?',
    'How can I learn to play the piano/synthesizer?',
    'Can I easily learn a piano at a later point if I start learning music with a keyboard initially?',
]
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

#### Unnamed Dataset


* Size: 50,881 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        | sentence_2                                                                        |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 13.59 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.56 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.67 tokens</li><li>max: 52 tokens</li></ul> |
* Samples:
  | sentence_0                                              | sentence_1                                                                               | sentence_2                                                                                    |
  |:--------------------------------------------------------|:-----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|
  | <code>What does Donald Trump think of India?</code>     | <code>Donald Trump: What is Donald Trump's take on India? Will it affect Indians?</code> | <code>How is the presidency of Donald Trump going to affect India's IT industry?</code>       |
  | <code>What is the best way to whiten your teeth?</code> | <code>What can I do to whiten my teeth?</code>                                           | <code>Can you get teeth whitening even if you have a cavity?</code>                           |
  | <code>How can we meet to PM Narendra Modi?</code>       | <code>How can I meet Narendra Modi if it's very important?</code>                        | <code>How can I contact PM Narendra Modi Ji if I know anyone who may have black money?</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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin

#### 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`: 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`: 10
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step  | Training Loss |
|:------:|:-----:|:-------------:|
| 0.3143 | 500   | 3.4002        |
| 0.6285 | 1000  | 1.4741        |
| 0.9428 | 1500  | 1.0103        |
| 1.2571 | 2000  | 0.7645        |
| 1.5713 | 2500  | 0.6256        |
| 1.8856 | 3000  | 0.5197        |
| 2.1999 | 3500  | 0.4278        |
| 2.5141 | 4000  | 0.3611        |
| 2.8284 | 4500  | 0.2858        |
| 3.1427 | 5000  | 0.236         |
| 3.4569 | 5500  | 0.2013        |
| 3.7712 | 6000  | 0.1623        |
| 4.0855 | 6500  | 0.1395        |
| 4.3997 | 7000  | 0.1112        |
| 4.7140 | 7500  | 0.1033        |
| 5.0283 | 8000  | 0.0853        |
| 5.3426 | 8500  | 0.0716        |
| 5.6568 | 9000  | 0.0644        |
| 5.9711 | 9500  | 0.0577        |
| 6.2854 | 10000 | 0.0522        |
| 6.5996 | 10500 | 0.0444        |
| 6.9139 | 11000 | 0.0417        |
| 7.2282 | 11500 | 0.0328        |
| 7.5424 | 12000 | 0.0326        |
| 7.8567 | 12500 | 0.0326        |
| 8.1710 | 13000 | 0.0267        |
| 8.4852 | 13500 | 0.0234        |
| 8.7995 | 14000 | 0.025         |
| 9.1138 | 14500 | 0.0224        |
| 9.4280 | 15000 | 0.0198        |
| 9.7423 | 15500 | 0.0206        |


### Framework Versions
- Python: 3.12.6
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.0

## 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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