sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:100000 loss:CosineSimilarityLoss arxiv:1908.10084 base_model:sentence-transformers/all-MiniLM-L12-v2 base_model:finetune:sentence-transformers/all-MiniLM-L12-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
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: Face off with a ref mid-hockey game in an arena.
  sentences:
  - Nobody is playing
  - A mustached man in a patterned shirt watches a boat painted blue and orange.
  - Two adults makes calls on there cell phones during there lunch breaks.
- source_sentence: A group of people, one holding a yellow and blue umbrella, are
    standing at the top of some stairs.
  sentences:
  - One person wields an umbrella.
  - A girl is on the beach.
  - A man is on his couch.
- source_sentence: A man waiting for the results of the machine after doing an experiment
    in his laboratory.
  sentences:
  - There is a man playing an instrument while running
  - A man in a lab waits to get more information about his experiment.
  - The graffiti artists admire their work.
- source_sentence: People in a tent shelter near the bottom of stairs.
  sentences:
  - A boy has fallen asleep during dinner.
  - Three men address a crowd.
  - People are in a makeshift shelter at the foot of a staircase.
- source_sentence: A female researcher looking through a microscope.
  sentences:
  - A man misses the rope and falls
  - A small girl is playing video games
  - A woman is researching with a microscope.
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: 0.48994508338253345
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.4778683474663533
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.46917600703738915
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.47754796729416876
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.46924620767742137
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.4778683474663533
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.48994508631435785
      name: Pearson Dot
    - type: spearman_dot
      value: 0.4778683472855999
      name: Spearman Dot
    - type: pearson_max
      value: 0.48994508631435785
      name: Pearson Max
    - type: spearman_max
      value: 0.4778683474663533
      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-snli-MiniLM-L12-v2")
# Run inference
sentences = [
    'A female researcher looking through a microscope.',
    'A woman is researching with a microscope.',
    'A small girl is playing video games',
]
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     | 0.4899     |
| spearman_cosine    | 0.4779     |
| pearson_manhattan  | 0.4692     |
| spearman_manhattan | 0.4775     |
| pearson_euclidean  | 0.4692     |
| spearman_euclidean | 0.4779     |
| pearson_dot        | 0.4899     |
| spearman_dot       | 0.4779     |
| pearson_max        | 0.4899     |
| **spearman_max**   | **0.4779** |

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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: 16.32 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.46 tokens</li><li>max: 32 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 man wearing jeans and a t-shirt plays guitar for a smiling woman and child as they sit on a staircase near red and orange balloons.</code> | <code>A man is in jail.</code>                     | <code>1.0</code> |
  | <code>A boy wearing blue short standing on the traffic signal pole.</code>                                                                         | <code>The boy is carrying his school books.</code> | <code>0.5</code> |
  | <code>Several people on a busy street or perhaps at a fair.</code>                                                                                 | <code>They are walkng.</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   | 0.1832        | 0.3114                |
| 0.16   | 1000  | 0.1489        | 0.3518                |
| 0.24   | 1500  | 0.1468        | 0.3697                |
| 0.32   | 2000  | 0.1411        | 0.3723                |
| 0.4    | 2500  | 0.14          | 0.4062                |
| 0.48   | 3000  | 0.1366        | 0.3923                |
| 0.56   | 3500  | 0.1379        | 0.4143                |
| 0.64   | 4000  | 0.1357        | 0.3928                |
| 0.72   | 4500  | 0.1331        | 0.4067                |
| 0.8    | 5000  | 0.1338        | 0.4293                |
| 0.88   | 5500  | 0.1294        | 0.4183                |
| 0.96   | 6000  | 0.1305        | 0.4402                |
| 1.0    | 6250  | -             | 0.4454                |
| 1.04   | 6500  | 0.1303        | 0.4408                |
| 1.12   | 7000  | 0.1275        | 0.4416                |
| 1.2    | 7500  | 0.1285        | 0.4287                |
| 1.28   | 8000  | 0.125         | 0.4404                |
| 1.3600 | 8500  | 0.1253        | 0.4408                |
| 1.44   | 9000  | 0.1246        | 0.4293                |
| 1.52   | 9500  | 0.126         | 0.4535                |
| 1.6    | 10000 | 0.1257        | 0.4455                |
| 1.6800 | 10500 | 0.1264        | 0.4520                |
| 1.76   | 11000 | 0.1248        | 0.4526                |
| 1.8400 | 11500 | 0.1208        | 0.4631                |
| 1.92   | 12000 | 0.1236        | 0.4635                |
| 2.0    | 12500 | 0.1239        | 0.4573                |
| 2.08   | 13000 | 0.1209        | 0.4569                |
| 2.16   | 13500 | 0.1194        | 0.4642                |
| 2.24   | 14000 | 0.1206        | 0.4539                |
| 2.32   | 14500 | 0.117         | 0.4633                |
| 2.4    | 15000 | 0.1171        | 0.4657                |
| 2.48   | 15500 | 0.1181        | 0.4633                |
| 2.56   | 16000 | 0.1197        | 0.4552                |
| 2.64   | 16500 | 0.1182        | 0.4670                |
| 2.7200 | 17000 | 0.1155        | 0.4684                |
| 2.8    | 17500 | 0.1171        | 0.4640                |
| 2.88   | 18000 | 0.1139        | 0.4715                |
| 2.96   | 18500 | 0.1164        | 0.4769                |
| 3.0    | 18750 | -             | 0.4709                |
| 3.04   | 19000 | 0.1151        | 0.4704                |
| 3.12   | 19500 | 0.1144        | 0.4759                |
| 3.2    | 20000 | 0.1121        | 0.4795                |
| 3.2800 | 20500 | 0.1104        | 0.4697                |
| 3.36   | 21000 | 0.1127        | 0.4763                |
| 3.44   | 21500 | 0.1115        | 0.4742                |
| 3.52   | 22000 | 0.1126        | 0.4697                |
| 3.6    | 22500 | 0.1123        | 0.4735                |
| 3.68   | 23000 | 0.1132        | 0.4750                |
| 3.76   | 23500 | 0.1127        | 0.4743                |
| 3.84   | 24000 | 0.1086        | 0.4752                |
| 3.92   | 24500 | 0.1107        | 0.4781                |
| 4.0    | 25000 | 0.1114        | 0.4779                |


### 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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