sentence-transformers tensorboard safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:3696 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3696
- loss:MultipleNegativesRankingLoss
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: EmbeddingSimEval
      type: EmbeddingSimEval
    metrics:
    - type: pearson_cosine
      value: .nan
      name: Pearson Cosine
    - type: spearman_cosine
      value: .nan
      name: Spearman Cosine
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: BinaryClassifEval
      type: BinaryClassifEval
    metrics:
    - type: cosine_accuracy
      value: 0.8
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.652718186378479
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.888888888888889
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.652718186378479
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 1.0
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8
      name: Cosine Recall
    - type: cosine_ap
      value: 1.0
      name: Cosine Ap
    - type: cosine_mcc
      value: 0.0
      name: Cosine Mcc
---

# SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
<!-- - **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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("model")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Semantic Similarity

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

| Metric              | Value   |
|:--------------------|:--------|
| pearson_cosine      | nan     |
| **spearman_cosine** | **nan** |

#### Binary Classification

* Dataset: `BinaryClassifEval`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                    | Value   |
|:--------------------------|:--------|
| cosine_accuracy           | 0.8     |
| cosine_accuracy_threshold | 0.6527  |
| cosine_f1                 | 0.8889  |
| cosine_f1_threshold       | 0.6527  |
| cosine_precision          | 1.0     |
| cosine_recall             | 0.8     |
| **cosine_ap**             | **1.0** |
| cosine_mcc                | 0.0     |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 3,696 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | label                        |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------|
  | type    | string                                                                            | string                                                                            | int                          |
  | details | <ul><li>min: 37 tokens</li><li>mean: 40.4 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 49 tokens</li><li>mean: 62.2 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
* Samples:
  | sentence1                                                                                                                                                      | sentence2                                                                                                                                                                                                                                                                                              | label          |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>Quelle proportion des dépenses pour l'installation de bornes de recharge électrique peut être couverte par la région Île-de-France?</code>               | <code>Nature de l'aide: La Région participera à hauteur de 50% de la dépense supportée par le maître d’ouvrage plafonnée en fonction du type de bornes</code>                                                                                                                                          | <code>1</code> |
  | <code>Quels types de projets sont éligibles pour obtenir un financement de la Région Île-de-France dans le cadre du développement de l'électromobilité?</code> | <code>Type de project: Le dispositif a pour objet le financement : Des études d’élaboration d’un document stratégique,De l’installation ou la mise à niveau des IRVE situées sur le domaine public francilien, respectant les critères du label régional et s’inscrivant dans un plan d’actions</code> | <code>1</code> |
  | <code>Quelle est la démarche à suivre pour déposer une demande de subvention concernant l'électromobilité en Île-de-France?</code>                             | <code>Procédures et démarches: Déposez sur mesdemarches.iledefrance.fr votre dossier de demande de subvention présentant le projet de manière précise et comportant toutes les pièces permettant l’instruction du dossier, réputé complet, par les services de la Région</code>                        | <code>1</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"
  }
  ```

### Evaluation Dataset

#### json

* Dataset: json
* Size: 687 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 687 samples:
  |         | sentence1                                                                         | sentence2                                                                          | label                        |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------|
  | type    | string                                                                            | string                                                                             | int                          |
  | details | <ul><li>min: 24 tokens</li><li>mean: 33.6 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 90.4 tokens</li><li>max: 257 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
* Samples:
  | sentence1                                                                                                                                                 | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | label          |
  |:----------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>Sous quelles conditions mon centre de formation en apprentissage peut-il être éligible à une subvention pour des investissements?</code>            | <code>Le dispositif est accessible à tous les OFA sous réserve de remplir les 5 conditions suivantes : Dispenser une activité apprentissage ayant obtenu une certification,Dispenser des formations en apprentissage sur le territoire francilien depuis au moins 1 an en qualité de CFA, d’OFA ou d’UFA,Présenter un projet d’investissement prévu pour la dispense de formations en apprentissage sur le territoire francilien,Être propriétaire du bien pour lequel une subvention est sollicitée ou titulaire d’un bail récemment renouvelé (ou engagement du propriétaire à renouveler le bail), en propre ou sous la forme de SCI, et assurant la maîtrise d’ouvrage des travaux d’investissement,Présenter un besoin de financement sur le projet d’investissement ne pouvant être pris en charge au titre des fonds propres de la structure et de tiers financeurs</code> | <code>1</code> |
  | <code>Est-ce que ma structure qui dispense des formations en apprentissage doit avoir une certaine ancienneté pour bénéficier de l'aide régionale?</code> | <code>Dispenser des formations en apprentissage sur le territoire francilien depuis au moins 1 an en qualité de CFA, d’OFA ou d’UFA</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | <code>1</code> |
  | <code>Comment dois-je procéder pour soumettre ma demande de soutien à l'investissement pour mon organisme de formation?</code>                            | <code>L'organisme doit déposer sa demande et les pièces justificatives via le portail mesdemarches.iledefrance.fr</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | <code>1</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"
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 2
- `per_device_eval_batch_size`: 2
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 2
- `per_device_eval_batch_size`: 2
- `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`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: 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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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_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
- `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
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | Validation Loss | EmbeddingSimEval_spearman_cosine | BinaryClassifEval_cosine_ap |
|:-----:|:----:|:---------------:|:--------------------------------:|:---------------------------:|
| 1.0   | 3    | 0.2267          | nan                              | 1.0                         |
| 2.0   | 6    | 0.2448          | nan                              | 1.0                         |


### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.3.0
- Accelerate: 1.1.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0

## 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{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->