README.md

---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:100000
- loss:MultipleNegativesRankingLoss
base_model: unsloth/all-MiniLM-L6-v2
widget:
- source_sentence: the three boys are all holding onto a flotation device in the water.
  sentences:
  - Three boys are in a body of water.
  - A school band is playing.
  - There is an animal in water
- source_sentence: A man on a street in a bright t-shirt holds some sort of tablet
    towards a woman in a pink t-shirt and shades.
  sentences:
  - A man is showing a woman something
  - People are outside in the snow.
  - Cheerleaders cheer on a field for an activity.
- source_sentence: A young woman is drawing with a Sharpie marker.
  sentences:
  - A car is flooding.
  - The woman is drawing
  - A dog with an object in it's mouth is in the water.
- source_sentence: A baseball player is putting all his might in to throwing a ball.
  sentences:
  - There are people at work.
  - One man with a bat wearing red and white.
  - Pitcher is winding up a throw
- source_sentence: Five men, one wearing a white shirt standing on something, hanging
    up a picture of a child.
  sentences:
  - The people are outdoors eating and drinking.
  - Two workers are opening a utility box.
  - A group of men are hanging a picture on a wall.
datasets:
- sentence-transformers/all-nli
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on unsloth/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [unsloth/all-MiniLM-L6-v2](https://huggingface.co/unsloth/all-MiniLM-L6-v2) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [unsloth/all-MiniLM-L6-v2](https://huggingface.co/unsloth/all-MiniLM-L6-v2) <!-- at revision 0f79ca30c044e92859f5852d3a29fb6e976741cd -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modality:** Text
- **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/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'PeftModelForFeatureExtraction'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', 'include_prompt': True})
  (2): Normalize({})
)
```

## 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("jaswanthsanjay88/mini_embedding_lora")
# Run inference
sentences = [
    'Five men, one wearing a white shirt standing on something, hanging up a picture of a child.',
    'A group of men are hanging a picture on a wall.',
    'Two workers are opening a utility box.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6383, 0.0003],
#         [0.6383, 1.0000, 0.0730],
#         [0.0003, 0.0730, 1.0000]])
```
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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: 100,000 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 100 samples:
  |          | anchor                                                                           | positive                                                                          |
  |:---------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type     | string                                                                           | string                                                                            |
  | modality | text                                                                             | text                                                                              |
  | details  | <ul><li>min: 8 tokens</li><li>mean: 18.2 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.22 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
  | anchor                                                                     | positive                                         |
  |:---------------------------------------------------------------------------|:-------------------------------------------------|
  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   |
  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          |
  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</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",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  ```

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

- `per_device_train_batch_size`: 256
- `learning_rate`: 0.0002
- `num_train_epochs`: 2
- `warmup_ratio`: 0.03
- `fp16`: True
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 8
- `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`: 0.0002
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.03
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `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
- `hub_revision`: None
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.1279 | 50   | 0.734         |
| 0.2558 | 100  | 0.7267        |
| 0.3836 | 150  | 0.7068        |
| 0.5115 | 200  | 0.6877        |
| 0.6394 | 250  | 0.6978        |
| 0.7673 | 300  | 0.6905        |
| 0.8951 | 350  | 0.6856        |
| 1.0230 | 400  | 0.6764        |
| 1.1509 | 450  | 0.6737        |
| 1.2788 | 500  | 0.6521        |
| 1.4066 | 550  | 0.6718        |
| 1.5345 | 600  | 0.6322        |
| 1.6624 | 650  | 0.6584        |
| 1.7903 | 700  | 0.614         |
| 1.9182 | 750  | 0.6485        |


### Training Time
- **Training**: 6.8 minutes

### Framework Versions
- Python: 3.12.13
- Sentence Transformers: 5.6.0
- Transformers: 4.56.2
- PyTorch: 2.11.0+cu128
- Accelerate: 1.14.0
- Datasets: 4.3.0
- Tokenizers: 0.22.2

## 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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}
```

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