README.md

---
base_model: sentence-transformers/all-MiniLM-L12-v2
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:100000
- loss:CosineSimilarityLoss
widget:
- source_sentence: A boy wearing climbing gear climbs by a wooden pole.
  sentences:
  - A person wearing climbing gear climbs by a wooden pole.
  - A man holds up a tent pole.
  - A man plays an instrument.
- source_sentence: Asian men saying hello to each other.
  sentences:
  - Asian men are about to attend a convention.
  - One man is working on a wrist watch to repair it.
  - A white male dog is following a black female dog because she is in heat.
- source_sentence: A woman in a white shirt and red jeans is carrying a plastic bag
    and cellphone while walking along the street by art prints.
  sentences:
  - The people are sitting on a couch
  - The man is walking down the street with a plastic bag.
  - A man wants to join in the conversation
- source_sentence: Girl in a thin rowboat leaving the dock of a lake.
  sentences:
  - A man in a solid white shirt and two black-haired boys pose for pictures inside.
  - The ladies are having a conversation.
  - The girl is sitting on the shore of the lake.
- source_sentence: A large crowd watches as a couple tap dances together on a wooden
    floor.
  sentences:
  - People are leaving the restaurant.
  - A man crashes his car into the grocery store.
  - A man swings a golf club.
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: snli dev
      type: snli-dev
    metrics:
    - type: pearson_cosine
      value: 0.5007411996817115
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.49310662404125943
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.4737846265333258
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.4923216703895389
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.47496147875492195
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.4931066240443629
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.500741200773276
      name: Pearson Dot
    - type: spearman_dot
      value: 0.49310655847757945
      name: Spearman Dot
    - type: pearson_max
      value: 0.500741200773276
      name: Pearson Max
    - type: spearman_max
      value: 0.4931066240443629
      name: Spearman Max
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-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-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision 30ce63ae64e71b9199b3d2eae9de99f64a26eedc -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 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': 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})
  (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("Nessrine9/finetuned2-MiniLM-L12-v2")
# Run inference
sentences = [
    'A large crowd watches as a couple tap dances together on a wooden floor.',
    'A man swings a golf club.',
    'A man crashes his car into the grocery store.',
]
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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## Evaluation

### Metrics

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

| Metric             | Value      |
|:-------------------|:-----------|
| pearson_cosine     | 0.5007     |
| spearman_cosine    | 0.4931     |
| pearson_manhattan  | 0.4738     |
| spearman_manhattan | 0.4923     |
| pearson_euclidean  | 0.475      |
| spearman_euclidean | 0.4931     |
| pearson_dot        | 0.5007     |
| spearman_dot       | 0.4931     |
| pearson_max        | 0.5007     |
| **spearman_max**   | **0.4931** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 100,000 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                                                                            | float                                                         |
  | details | <ul><li>min: 7 tokens</li><li>mean: 16.85 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.61 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                                             | sentence_1                                                             | label            |
  |:---------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:-----------------|
  | <code>A biker is practicing a trick while his friend watch him as his audience.</code> | <code>man riding the bike to show his talent to his girlfriend.</code> | <code>0.5</code> |
  | <code>A man in a brown jacket standing in front of an open porch door.</code>          | <code>A man is standing in front of the porch door.</code>             | <code>0.0</code> |
  | <code>Two men and three children are at the beach.</code>                              | <code>Five people enjoying their vacation.</code>                      | <code>0.5</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 4
- `fp16`: True
- `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`: 4
- `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`: 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}
- `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 | snli-dev_spearman_max |
|:------:|:-----:|:-------------:|:---------------------:|
| 0.08   | 500   | 0.1807        | 0.3001                |
| 0.16   | 1000  | 0.1497        | 0.3646                |
| 0.24   | 1500  | 0.1443        | 0.3652                |
| 0.32   | 2000  | 0.1394        | 0.3860                |
| 0.4    | 2500  | 0.1369        | 0.3810                |
| 0.48   | 3000  | 0.1346        | 0.3895                |
| 0.56   | 3500  | 0.1358        | 0.4147                |
| 0.64   | 4000  | 0.1387        | 0.4190                |
| 0.72   | 4500  | 0.131         | 0.4254                |
| 0.8    | 5000  | 0.1314        | 0.4219                |
| 0.88   | 5500  | 0.1288        | 0.4342                |
| 0.96   | 6000  | 0.1299        | 0.4135                |
| 1.0    | 6250  | -             | 0.4393                |
| 1.04   | 6500  | 0.1306        | 0.4565                |
| 1.12   | 7000  | 0.1253        | 0.4433                |
| 1.2    | 7500  | 0.1275        | 0.4486                |
| 1.28   | 8000  | 0.1265        | 0.4616                |
| 1.3600 | 8500  | 0.1237        | 0.4462                |
| 1.44   | 9000  | 0.1223        | 0.4573                |
| 1.52   | 9500  | 0.123         | 0.4609                |
| 1.6    | 10000 | 0.1251        | 0.4678                |
| 1.6800 | 10500 | 0.1262        | 0.4500                |
| 1.76   | 11000 | 0.1194        | 0.4696                |
| 1.8400 | 11500 | 0.1206        | 0.4733                |
| 1.92   | 12000 | 0.118         | 0.4701                |
| 2.0    | 12500 | 0.1238        | 0.4688                |
| 2.08   | 13000 | 0.1191        | 0.4646                |
| 2.16   | 13500 | 0.1179        | 0.4757                |
| 2.24   | 14000 | 0.1177        | 0.4652                |
| 2.32   | 14500 | 0.1176        | 0.4873                |
| 2.4    | 15000 | 0.115         | 0.4674                |
| 2.48   | 15500 | 0.1141        | 0.4784                |
| 2.56   | 16000 | 0.1143        | 0.4824                |
| 2.64   | 16500 | 0.1184        | 0.4898                |
| 2.7200 | 17000 | 0.1124        | 0.4818                |
| 2.8    | 17500 | 0.1141        | 0.4905                |
| 2.88   | 18000 | 0.1115        | 0.4850                |
| 2.96   | 18500 | 0.1123        | 0.4867                |
| 3.0    | 18750 | -             | 0.4867                |
| 3.04   | 19000 | 0.1149        | 0.4849                |
| 3.12   | 19500 | 0.1114        | 0.4888                |
| 3.2    | 20000 | 0.1124        | 0.4903                |
| 3.2800 | 20500 | 0.1124        | 0.4900                |
| 3.36   | 21000 | 0.1088        | 0.4871                |
| 3.44   | 21500 | 0.1065        | 0.4835                |
| 3.52   | 22000 | 0.1075        | 0.4912                |
| 3.6    | 22500 | 0.1115        | 0.4944                |
| 3.68   | 23000 | 0.1122        | 0.4932                |
| 3.76   | 23500 | 0.1074        | 0.4917                |
| 3.84   | 24000 | 0.1081        | 0.4923                |
| 3.92   | 24500 | 0.1057        | 0.4921                |
| 4.0    | 25000 | 0.1118        | 0.4931                |


### 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: 3.0.2
- 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",
}
```

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