README.md

---
base_model: sentence-transformers/all-MiniLM-L6-v2
datasets:
- sentence-transformers/stsb
language:
- en
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:101
- loss:CoSENTLoss
widget:
- source_sentence: The man is slicing a potato.
  sentences:
  - A woman is slicing carrot.
  - Two women are singing.
  - A man is slicing potato.
- source_sentence: A girl is playing a flute.
  sentences:
  - A woman stirs eggs in a bowl.
  - A girl plays a wind instrument.
  - A man is turning over tables in anger.
- source_sentence: People are playing baseball.
  sentences:
  - The cricket player hit the ball.
  - A man breaks a stick.
  - A woman is pouring a yellow mixture on a frying pan.
- source_sentence: A woman and man are riding in a car.
  sentences:
  - A woman driving a car is talking to the man seated beside her.
  - A woman is placing skewered food onto a cooker.
  - The man and woman are walking.
- source_sentence: A cat is on a robot.
  sentences:
  - A man is eating bread.
  - A woman is pouring eyes into a bowl.
  - A boy sits on a bed, sings and plays a guitar.
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.9186522039312566
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9276278198564623
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8991493568260668
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.9320766471557739
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9014580823459483
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.9289530024562572
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8789190604301875
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8957287815613981
      name: Spearman Dot
    - type: pearson_max
      value: 0.9186522039312566
      name: Pearson Max
    - type: spearman_max
      value: 0.9320766471557739
      name: Spearman Max
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **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': 384, '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("Husain/ramdam_fingerprint_embedding_model")
# Run inference
sentences = [
    'A cat is on a robot.',
    'A man is eating bread.',
    'A woman is pouring eyes into a bowl.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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You can finetune this model on your own dataset.

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

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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.9187     |
| **spearman_cosine** | **0.9276** |
| pearson_manhattan   | 0.8991     |
| spearman_manhattan  | 0.9321     |
| pearson_euclidean   | 0.9015     |
| spearman_euclidean  | 0.929      |
| pearson_dot         | 0.8789     |
| spearman_dot        | 0.8957     |
| pearson_max         | 0.9187     |
| spearman_max        | 0.9321     |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 101 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 101 samples:
  |         | sentence1                                                                        | sentence2                                                                        | score                                                          |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | float                                                          |
  | details | <ul><li>min: 7 tokens</li><li>mean: 9.44 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.46 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.66</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                  | sentence2                                                             | score             |
  |:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------|
  | <code>A plane is taking off.</code>                        | <code>An air plane is taking off.</code>                              | <code>1.0</code>  |
  | <code>A man is playing a large flute.</code>               | <code>A man is playing a flute.</code>                                | <code>0.76</code> |
  | <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Evaluation Dataset

#### stsb

* Dataset: [stsb](https://huggingface.co./datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co./datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                       | score                                                          |
  |:--------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                           | string                                                                          | float                                                          |
  | details | <ul><li>min: 6 tokens</li><li>mean: 9.35 tokens</li><li>max: 13 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 9.9 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.39</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                      | sentence2                                           | score             |
  |:-----------------------------------------------|:----------------------------------------------------|:------------------|
  | <code>A woman is riding on a horse.</code>     | <code>A man is turning over tables in anger.</code> | <code>0.0</code>  |
  | <code>A man is screwing wood to a wall.</code> | <code>A man is giving a woman a massage.</code>     | <code>0.04</code> |
  | <code>A girl is playing a flute.</code>        | <code>A girl plays a wind instrument.</code>        | <code>0.64</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `save_only_model`: True
- `seed`: 33
- `fp16`: True
- `load_best_model_at_end`: 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`: 8
- `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`: 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`: 10
- `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`: True
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 33
- `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`: 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`: proportional

</details>

### Training Logs
| Epoch    | Step    | loss       | sts-dev_spearman_cosine |
|:--------:|:-------:|:----------:|:-----------------------:|
| 0.1538   | 2       | 4.4641     | 0.9366                  |
| 0.3077   | 4       | 4.4652     | 0.9366                  |
| 0.4615   | 6       | 4.4719     | 0.9366                  |
| 0.6154   | 8       | 4.4903     | 0.9366                  |
| 0.7692   | 10      | 4.5264     | 0.9373                  |
| 0.9231   | 12      | 4.5954     | 0.9339                  |
| 1.0769   | 14      | 4.6832     | 0.9328                  |
| 1.2308   | 16      | 4.7534     | 0.9289                  |
| 1.3846   | 18      | 4.8155     | 0.9281                  |
| 1.5385   | 20      | 4.8788     | 0.9269                  |
| 1.6923   | 22      | 4.9350     | 0.9272                  |
| 1.8462   | 24      | 4.9789     | 0.9239                  |
| 2.0      | 26      | 5.0132     | 0.9230                  |
| 2.1538   | 28      | 5.0636     | 0.9237                  |
| 2.3077   | 30      | 5.1068     | 0.9202                  |
| 2.4615   | 32      | 5.1460     | 0.9172                  |
| 2.6154   | 34      | 5.1602     | 0.9164                  |
| 2.7692   | 36      | 5.1493     | 0.9210                  |
| 2.9231   | 38      | 5.1399     | 0.9200                  |
| 3.0769   | 40      | 5.1342     | 0.9235                  |
| 3.2308   | 42      | 5.1413     | 0.9258                  |
| 3.3846   | 44      | 5.1440     | 0.9271                  |
| 3.5385   | 46      | 5.1583     | 0.9311                  |
| 3.6923   | 48      | 5.1664     | 0.9293                  |
| 3.8462   | 50      | 5.1682     | 0.9293                  |
| 4.0      | 52      | 5.1617     | 0.9293                  |
| 4.1538   | 54      | 5.1543     | 0.9293                  |
| 4.3077   | 56      | 5.1480     | 0.9293                  |
| 4.4615   | 58      | 5.1428     | 0.9291                  |
| 4.6154   | 60      | 5.1292     | 0.9298                  |
| 4.7692   | 62      | 5.1271     | 0.9276                  |
| 4.9231   | 64      | 5.1133     | 0.9276                  |
| 5.0769   | 66      | 5.0928     | 0.9270                  |
| 5.2308   | 68      | 5.0874     | 0.9270                  |
| 5.3846   | 70      | 5.0755     | 0.9270                  |
| 5.5385   | 72      | 5.0665     | 0.9270                  |
| 5.6923   | 74      | 5.0676     | 0.9293                  |
| 5.8462   | 76      | 5.0747     | 0.9293                  |
| 6.0      | 78      | 5.0647     | 0.9295                  |
| 6.1538   | 80      | 5.0763     | 0.9273                  |
| 6.3077   | 82      | 5.0832     | 0.9272                  |
| 6.4615   | 84      | 5.0750     | 0.9289                  |
| 6.6154   | 86      | 5.0547     | 0.9289                  |
| 6.7692   | 88      | 5.0350     | 0.9308                  |
| 6.9231   | 90      | 5.0221     | 0.9308                  |
| 7.0769   | 92      | 5.0107     | 0.9308                  |
| 7.2308   | 94      | 4.9967     | 0.9297                  |
| 7.3846   | 96      | 4.9983     | 0.9297                  |
| 7.5385   | 98      | 5.0026     | 0.9277                  |
| 7.6923   | 100     | 5.0095     | 0.9277                  |
| 7.8462   | 102     | 5.0102     | 0.9277                  |
| 8.0      | 104     | 5.0055     | 0.9271                  |
| 8.1538   | 106     | 5.0031     | 0.9271                  |
| 8.3077   | 108     | 4.9976     | 0.9271                  |
| 8.4615   | 110     | 4.9941     | 0.9271                  |
| 8.6154   | 112     | 4.9856     | 0.9276                  |
| 8.7692   | 114     | 4.9821     | 0.9276                  |
| 8.9231   | 116     | 4.9782     | 0.9276                  |
| 9.0769   | 118     | 4.9706     | 0.9276                  |
| 9.2308   | 120     | 4.9646     | 0.9276                  |
| 9.3846   | 122     | 4.9584     | 0.9276                  |
| 9.5385   | 124     | 4.9537     | 0.9276                  |
| 9.6923   | 126     | 4.9499     | 0.9276                  |
| 9.8462   | 128     | 4.9485     | 0.9276                  |
| **10.0** | **130** | **4.9463** | **0.9276**              |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.8.10
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.3.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.0
- 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",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

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