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---
language:
- fr
tags:
- sentence-transformers
- sparse-encoder
- sparse
- csr
- generated_from_trainer
- dataset_size:12227
- loss:SpladeLoss
- loss:SparseCosineSimilarityLoss
- loss:FlopsLoss
base_model: almanach/camembert-large
widget:
- text: Une femme, un petit garçon et un petit bébé se tiennent devant une statue
de vache.
- text: En anglais, l'utilisation la plus courante de do est certainement Do-Support.
- text: Je ne pense pas que la charge de la preuve repose sur des versions positives
ou négatives.
- text: Cinq lévriers courent sur une piste de sable.
- text: J'envisage de dépenser les 48 dollars par mois pour le système GTD (Getting
things done) annoncé par David Allen.
datasets:
- CATIE-AQ/frenchSTS
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- active_dims
- sparsity_ratio
model-index:
- name: CSR Sparse Encoder
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.730659053269462
name: Pearson Cosine
- type: spearman_cosine
value: 0.7229701164875609
name: Spearman Cosine
- type: active_dims
value: 239.04523468017578
name: Active Dims
- type: sparsity_ratio
value: 0.9416393470019102
name: Sparsity Ratio
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.7536670661877773
name: Pearson Cosine
- type: spearman_cosine
value: 0.7255882185109458
name: Spearman Cosine
- type: active_dims
value: 229.7224884033203
name: Active Dims
- type: sparsity_ratio
value: 0.9439154081046581
name: Sparsity Ratio
---
# CSR Sparse Encoder
This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [almanach/camembert-large](https://huggingface.co/almanach/camembert-large) on the [french_sts](https://huggingface.co/datasets/CATIE-AQ/frenchSTS) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space with 256 maximum active dimensions and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** CSR Sparse Encoder
- **Base model:** [almanach/camembert-large](https://huggingface.co/almanach/camembert-large) <!-- at revision df7dbf5 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 4096 dimensions (trained with 256 maximum active dimensions)
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [french_sts](https://huggingface.co/datasets/CATIE-AQ/frenchSTS)
- **Language:** fr
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
### Full Model Architecture
```
SparseEncoder(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'CamembertModel'})
(1): Pooling({'word_embedding_dimension': 1024, '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): SparseAutoEncoder({'input_dim': 1024, 'hidden_dim': 4096, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30})
)
```
## 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 SparseEncoder
# Download from the 🤗 Hub
model = SparseEncoder("bourdoiscatie/SPLADE_camembert-large_STS")
# Run inference
sentences = [
"Oui, je peux vous dire d'après mon expérience personnelle qu'ils ont certainement sifflé.",
"Il est vrai que les bombes de la Seconde Guerre mondiale faisaient un bruit de sifflet lorsqu'elles tombaient.",
"J'envisage de dépenser les 48 dollars par mois pour le système GTD (Getting things done) annoncé par David Allen.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 4096]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.3673, 0.2794],
# [0.3673, 1.0000, 0.2023],
# [0.2794, 0.2023, 1.0000]])
```
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## Evaluation
### Metrics
#### Semantic Similarity
* Datasets: `sts-dev` and `sts-test`
* Evaluated with [<code>SparseEmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseEmbeddingSimilarityEvaluator)
| Metric | sts-dev | sts-test |
|:--------------------|:----------|:-----------|
| pearson_cosine | 0.7307 | 0.7537 |
| **spearman_cosine** | **0.723** | **0.7256** |
| active_dims | 239.0452 | 229.7225 |
| sparsity_ratio | 0.9416 | 0.9439 |
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## Training Details
### Training Dataset
#### french_sts
* Dataset: [french_sts](https://huggingface.co/datasets/CATIE-AQ/frenchSTS) at [47128cc](https://huggingface.co/datasets/CATIE-AQ/frenchSTS/tree/47128cc18c893e5b93679037cdca303849e05309)
* Size: 12,227 training 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: 11.75 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.79 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:----------------------------------------------------|:----------------------------------------------------|:---------------------------------|
| <code>Un avion est en train de décoller.</code> | <code>Un avion est en train de décoller.</code> | <code>1.0</code> |
| <code>Un homme est en train de fumer.</code> | <code>Un homme fait du patinage.</code> | <code>0.10000000149011612</code> |
| <code>Une personne jette un chat au plafond.</code> | <code>Une personne jette un chat au plafond.</code> | <code>1.0</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseCosineSimilarityLoss(loss_fct='torch.nn.modules.loss.MSELoss')",
"document_regularizer_weight": 0.003
}
```
### Evaluation Dataset
#### french_sts
* Dataset: [french_sts](https://huggingface.co/datasets/CATIE-AQ/frenchSTS) at [47128cc](https://huggingface.co/datasets/CATIE-AQ/frenchSTS/tree/47128cc18c893e5b93679037cdca303849e05309)
* Size: 3,526 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: 19.13 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.05 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.43</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------------|:----------------------------------------------------------------------------|:-------------------------------|
| <code>Un homme avec un casque de sécurité est en train de danser.</code> | <code>Un homme portant un casque de sécurité est en train de danser.</code> | <code>1.0</code> |
| <code>Un jeune enfant monte à cheval.</code> | <code>Un enfant monte à cheval.</code> | <code>0.949999988079071</code> |
| <code>Un homme donne une souris à un serpent.</code> | <code>L'homme donne une souris au serpent.</code> | <code>1.0</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseCosineSimilarityLoss(loss_fct='torch.nn.modules.loss.MSELoss')",
"document_regularizer_weight": 0.003
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `bf16`: True
#### 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`: 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`: 1.0
- `num_train_epochs`: 3
- `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`: True
- `fp16`: False
- `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}
- `tp_size`: 0
- `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`: 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
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|
| -1 | -1 | - | - | 0.4890 | - |
| 0.1307 | 100 | 0.0458 | - | - | - |
| 0.2614 | 200 | 0.0447 | - | - | - |
| 0.3922 | 300 | 0.0468 | - | - | - |
| 0.5229 | 400 | 0.0416 | - | - | - |
| 0.6536 | 500 | 0.0398 | - | - | - |
| 0.7843 | 600 | 0.0397 | - | - | - |
| 0.9150 | 700 | 0.0398 | - | - | - |
| 1.0 | 765 | - | 0.0417 | 0.6801 | - |
| 1.0458 | 800 | 0.0368 | - | - | - |
| 1.1765 | 900 | 0.0296 | - | - | - |
| 1.3072 | 1000 | 0.0288 | - | - | - |
| 1.4379 | 1100 | 0.0285 | - | - | - |
| 1.5686 | 1200 | 0.0264 | - | - | - |
| 1.6993 | 1300 | 0.0251 | - | - | - |
| 1.8301 | 1400 | 0.0256 | - | - | - |
| 1.9608 | 1500 | 0.0253 | - | - | - |
| 2.0 | 1530 | - | 0.0368 | 0.7083 | - |
| 2.0915 | 1600 | 0.0197 | - | - | - |
| 2.2222 | 1700 | 0.0151 | - | - | - |
| 2.3529 | 1800 | 0.0156 | - | - | - |
| 2.4837 | 1900 | 0.0155 | - | - | - |
| 2.6144 | 2000 | 0.0141 | - | - | - |
| 2.7451 | 2100 | 0.0134 | - | - | - |
| 2.8758 | 2200 | 0.0137 | - | - | - |
| 3.0 | 2295 | - | 0.0352 | 0.7230 | - |
| -1 | -1 | - | - | - | 0.7256 |
### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 5.0.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 2.16.0
- 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",
}
```
#### SpladeLoss
```bibtex
@misc{formal2022distillationhardnegativesampling,
title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
year={2022},
eprint={2205.04733},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2205.04733},
}
```
#### FlopsLoss
```bibtex
@article{paria2020minimizing,
title={Minimizing flops to learn efficient sparse representations},
author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
journal={arXiv preprint arXiv:2004.05665},
year={2020}
}
```
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