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: Two young men playing at a computer.
  sentences:
  - Two boys are competing in a game.
  - A man is sleeping.
  - a boy rides a skateboard near a building
- source_sentence: A man with a hat and long gray beard, wearing cross, is holding
    a napkin and striped box.
  sentences:
  - The man is holding an item.
  - The street is dirty.
  - A red boat approaches a river bank.
- source_sentence: People clap as a well dressed man and woman walk through a room
    holding hands.
  sentences:
  - A man falls into the water.
  - The crowd claps at the couple holding hands.
  - There is a squirrel that jumps.
- source_sentence: A man and two boys are filtering water near their campsite in the
    woods.
  sentences:
  - A man looks for criminal activity in the dark streets.
  - The child was performing a stunt on the bike.
  - The people are filtering water for their camp
- source_sentence: Many people outside on bicycles.
  sentences:
  - the young man is wearing a black t-shirt modeled after a tuxedo
  - Protesters are in the back of a photo with a magazine display in the foreground.
  - People are riding bikes in a race.
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: 0.5041089229469013
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.49624988336246095
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.48476324482316935
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.49567540413897415
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.48548959313285095
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.49624986145166594
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5041089211722365
      name: Pearson Dot
    - type: spearman_dot
      value: 0.4962498830110755
      name: Spearman Dot
    - type: pearson_max
      value: 0.5041089229469013
      name: Pearson Max
    - type: spearman_max
      value: 0.49624988336246095
      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 a05860a77cef7b37e0048a7864658139bc18a854 -->
- **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("cherifkhalifah/finetuned2-snli-MiniLM-L12-v2")
# Run inference
sentences = [
    'Many people outside on bicycles.',
    'People are riding bikes in a race.',
    'Protesters are in the back of a photo with a magazine display in the foreground.',
]
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     | 0.5041     |
| spearman_cosine    | 0.4962     |
| pearson_manhattan  | 0.4848     |
| spearman_manhattan | 0.4957     |
| pearson_euclidean  | 0.4855     |
| spearman_euclidean | 0.4962     |
| pearson_dot        | 0.5041     |
| spearman_dot       | 0.4962     |
| pearson_max        | 0.5041     |
| **spearman_max**   | **0.4962** |

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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: 16.36 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.62 tokens</li><li>max: 33 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 mother and two children opening gifts on a Christmas morning.</code>          | <code>A mother and children cut into a large pizza.</code> | <code>1.0</code> |
  | <code>Two men in protective gear are in a speed car racing to the finish line.</code> | <code>Two boys are playing tag.</code>                     | <code>1.0</code> |
  | <code>A person in a pink jacket is running onto the field.</code>                     | <code>The woman is running on to the field.</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   | 0.1782        | 0.3312                |
| 0.16   | 1000  | 0.1516        | 0.3393                |
| 0.24   | 1500  | 0.1422        | 0.3798                |
| 0.32   | 2000  | 0.1405        | 0.3675                |
| 0.4    | 2500  | 0.137         | 0.4029                |
| 0.48   | 3000  | 0.1398        | 0.3989                |
| 0.56   | 3500  | 0.136         | 0.4216                |
| 0.64   | 4000  | 0.1351        | 0.4322                |
| 0.72   | 4500  | 0.1317        | 0.4223                |
| 0.8    | 5000  | 0.1293        | 0.4331                |
| 0.88   | 5500  | 0.1318        | 0.4416                |
| 0.96   | 6000  | 0.1311        | 0.4185                |
| 1.0    | 6250  | -             | 0.4522                |
| 1.04   | 6500  | 0.129         | 0.4312                |
| 1.12   | 7000  | 0.1272        | 0.4544                |
| 1.2    | 7500  | 0.1271        | 0.4533                |
| 1.28   | 8000  | 0.125         | 0.4456                |
| 1.3600 | 8500  | 0.1229        | 0.4570                |
| 1.44   | 9000  | 0.1241        | 0.4529                |
| 1.52   | 9500  | 0.1254        | 0.4517                |
| 1.6    | 10000 | 0.1232        | 0.4563                |
| 1.6800 | 10500 | 0.1232        | 0.4565                |
| 1.76   | 11000 | 0.1198        | 0.4521                |
| 1.8400 | 11500 | 0.1201        | 0.4570                |
| 1.92   | 12000 | 0.1238        | 0.4758                |
| 2.0    | 12500 | 0.1195        | 0.4671                |
| 2.08   | 13000 | 0.1155        | 0.4582                |
| 2.16   | 13500 | 0.1208        | 0.4787                |
| 2.24   | 14000 | 0.1164        | 0.4733                |
| 2.32   | 14500 | 0.1164        | 0.4743                |
| 2.4    | 15000 | 0.1136        | 0.4733                |
| 2.48   | 15500 | 0.1177        | 0.4704                |
| 2.56   | 16000 | 0.1152        | 0.4711                |
| 2.64   | 16500 | 0.1162        | 0.4827                |
| 2.7200 | 17000 | 0.1136        | 0.4772                |
| 2.8    | 17500 | 0.1129        | 0.4853                |
| 2.88   | 18000 | 0.1161        | 0.4830                |
| 2.96   | 18500 | 0.1144        | 0.4827                |
| 3.0    | 18750 | -             | 0.4850                |
| 3.04   | 19000 | 0.112         | 0.4920                |
| 3.12   | 19500 | 0.1105        | 0.4901                |
| 3.2    | 20000 | 0.1122        | 0.4925                |
| 3.2800 | 20500 | 0.1114        | 0.4913                |
| 3.36   | 21000 | 0.1074        | 0.4887                |
| 3.44   | 21500 | 0.1093        | 0.4819                |
| 3.52   | 22000 | 0.1107        | 0.4853                |
| 3.6    | 22500 | 0.1088        | 0.4897                |
| 3.68   | 23000 | 0.1095        | 0.4922                |
| 3.76   | 23500 | 0.11          | 0.4923                |
| 3.84   | 24000 | 0.1075        | 0.4950                |
| 3.92   | 24500 | 0.1107        | 0.4967                |
| 4.0    | 25000 | 0.1073        | 0.4962                |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- 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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