Initial upload: camembert-large fine-tune for French construction matching (v2, 14k pairs)
01590b8 verified | tags: | |
| - sentence-transformers | |
| - sentence-similarity | |
| - feature-extraction | |
| - dense | |
| - generated_from_trainer | |
| - dataset_size:14481 | |
| - loss:MultipleNegativesRankingLoss | |
| base_model: Lajavaness/sentence-camembert-large | |
| widget: | |
| - source_sentence: Plomberie sanitaire | |
| sentences: | |
| - Semis manuel de pelouses à gazon, mauresques et ordinaires | |
| - interne | |
| - Installation sanitaire | |
| - source_sentence: Charpente bois | |
| sentences: | |
| - Structure charpente | |
| - Équipements sanitaires | |
| - Installation pour le briquetage des garnitures de frein | |
| - source_sentence: Machine à découper pour la découpe de la base des bandes et des | |
| plaques aiguilletées | |
| sentences: | |
| - AVB-915 | |
| - Touret d'affûtage pour bandes et plaques à aiguilles | |
| - section 200 x 400 mm | |
| - source_sentence: plus de 32 cm | |
| sentences: | |
| - combustible gaz-mazout, capacité de production de vapeur 35-75 t/h, pression 3,9 | |
| MPa | |
| - plus de 0,2 à 0,35 m3 | |
| - à la norme 01-02-104-01 | |
| - source_sentence: jusqu'à 25 m | |
| sentences: | |
| - à la norme 33-04-018-02 | |
| - 14,2 t | |
| - jusqu'à 50 m | |
| pipeline_tag: sentence-similarity | |
| library_name: sentence-transformers | |
| metrics: | |
| - pearson_cosine | |
| - spearman_cosine | |
| model-index: | |
| - name: SentenceTransformer based on Lajavaness/sentence-camembert-large | |
| results: | |
| - task: | |
| type: semantic-similarity | |
| name: Semantic Similarity | |
| dataset: | |
| name: eval | |
| type: eval | |
| metrics: | |
| - type: pearson_cosine | |
| value: .nan | |
| name: Pearson Cosine | |
| - type: spearman_cosine | |
| value: .nan | |
| name: Spearman Cosine | |
| # SentenceTransformer based on Lajavaness/sentence-camembert-large | |
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Lajavaness/sentence-camembert-large](https://huggingface.co/Lajavaness/sentence-camembert-large). It maps sentences & paragraphs to a 1024-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:** [Lajavaness/sentence-camembert-large](https://huggingface.co/Lajavaness/sentence-camembert-large) <!-- at revision 4d78b025607a4fa3803e994520dade7c337441b8 --> | |
| - **Maximum Sequence Length:** 514 tokens | |
| - **Output Dimensionality:** 1024 dimensions | |
| - **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/huggingface/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': 514, '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}) | |
| ) | |
| ``` | |
| ## 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("sentence_transformers_model_id") | |
| # Run inference | |
| sentences = [ | |
| "jusqu'à 25 m", | |
| "jusqu'à 50 m", | |
| 'à la norme 33-04-018-02', | |
| ] | |
| embeddings = model.encode(sentences) | |
| print(embeddings.shape) | |
| # [3, 1024] | |
| # Get the similarity scores for the embeddings | |
| similarities = model.similarity(embeddings, embeddings) | |
| print(similarities) | |
| # tensor([[1.0000, 0.8389, 0.0886], | |
| # [0.8389, 1.0000, 0.1294], | |
| # [0.0886, 0.1294, 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 | |
| * Dataset: `eval` | |
| * 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** | | |
| <!-- | |
| ## 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 | |
| #### Unnamed Dataset | |
| * Size: 14,481 training samples | |
| * Columns: <code>anchor</code> and <code>positive</code> | |
| * Approximate statistics based on the first 1000 samples: | |
| | | anchor | positive | | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | |
| | type | string | string | | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 13.16 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.46 tokens</li><li>max: 61 tokens</li></ul> | | |
| * Samples: | |
| | anchor | positive | | |
| |:-----------------------------------------------------------------------|:------------------------------------------------------------------------| | |
| | <code>Balances à plate-forme ; dispositif de recouvrement</code> | <code>Machine d'alumination</code> | | |
| | <code>plus de 18 m², coefficient de résistance des roches 4 - 6</code> | <code>plus de 18 m², coefficient de résistance des roches 7 - 20</code> | | |
| | <code>plus de 20 à 30 m dans les sols du groupe 1</code> | <code>plus de 20 à 30 m dans les sols du groupe 2</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", | |
| "gather_across_devices": false | |
| } | |
| ``` | |
| ### Evaluation Dataset | |
| #### Unnamed Dataset | |
| * Size: 1,609 evaluation samples | |
| * Columns: <code>anchor</code> and <code>positive</code> | |
| * Approximate statistics based on the first 1000 samples: | |
| | | anchor | positive | | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | |
| | type | string | string | | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 12.72 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.04 tokens</li><li>max: 64 tokens</li></ul> | | |
| * Samples: | |
| | anchor | positive | | |
| |:---------------------------------------|:---------------------------------------| | |
| | <code>10 m3, groupe de sols 3 m</code> | <code>15 m3, groupe de sols 1 m</code> | | |
| | <code>125-200 mm</code> | <code>250-400 mm</code> | | |
| | <code>à la norme 01-01-032-05</code> | <code>à la norme 01-01-032-06</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", | |
| "gather_across_devices": false | |
| } | |
| ``` | |
| ### Training Hyperparameters | |
| #### Non-Default Hyperparameters | |
| - `eval_strategy`: epoch | |
| - `per_device_train_batch_size`: 16 | |
| - `learning_rate`: 2e-05 | |
| - `num_train_epochs`: 5 | |
| - `warmup_steps`: 453 | |
| - `load_best_model_at_end`: 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`: 8 | |
| - `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`: 2e-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`: 5 | |
| - `max_steps`: -1 | |
| - `lr_scheduler_type`: linear | |
| - `lr_scheduler_kwargs`: None | |
| - `warmup_ratio`: 0.0 | |
| - `warmup_steps`: 453 | |
| - `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 | |
| - `bf16`: False | |
| - `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`: True | |
| - `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} | |
| - `parallelism_config`: 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 | |
| - `project`: huggingface | |
| - `trackio_space_id`: trackio | |
| - `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 | |
| - `hub_revision`: None | |
| - `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`: no | |
| - `neftune_noise_alpha`: None | |
| - `optim_target_modules`: None | |
| - `batch_eval_metrics`: False | |
| - `eval_on_start`: False | |
| - `use_liger_kernel`: False | |
| - `liger_kernel_config`: None | |
| - `eval_use_gather_object`: False | |
| - `average_tokens_across_devices`: True | |
| - `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 | eval_spearman_cosine | | |
| |:-------:|:--------:|:-------------:|:---------------:|:--------------------:| | |
| | 0.5 | 453 | 0.5925 | - | - | | |
| | 1.0 | 906 | 0.4408 | 0.2765 | nan | | |
| | 1.5 | 1359 | 0.3219 | - | - | | |
| | 2.0 | 1812 | 0.2956 | 0.2330 | nan | | |
| | 2.5 | 2265 | 0.1923 | - | - | | |
| | 3.0 | 2718 | 0.2017 | 0.2032 | nan | | |
| | 3.5 | 3171 | 0.1307 | - | - | | |
| | **4.0** | **3624** | **0.1151** | **0.1981** | **nan** | | |
| | 4.5 | 4077 | 0.096 | - | - | | |
| | 5.0 | 4530 | 0.0793 | 0.2025 | nan | | |
| * The bold row denotes the saved checkpoint. | |
| ### Framework Versions | |
| - Python: 3.9.6 | |
| - Sentence Transformers: 5.1.2 | |
| - Transformers: 4.57.6 | |
| - PyTorch: 2.8.0 | |
| - Accelerate: 1.10.1 | |
| - Datasets: 4.5.0 | |
| - Tokenizers: 0.22.2 | |
| ## 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} | |
| } | |
| ``` | |
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