| | --- |
| | 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]]) |
| | ``` |
| |
|
| | <!-- |
| | ### 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 |
| |
|
| | * 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 | |
| | |
| | <!-- |
| | ## 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 |
| | |
| | #### 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} |
| | } |
| | ``` |
| |
|
| | <!-- |
| | ## 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.* |
| | --> |
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| | ## Model Card Contact |
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| | *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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