| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:4500 |
| - loss:CosineSimilarityLoss |
| base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| widget: |
| - source_sentence: Honnêtement, mon stress est modéré, je gère globalement bien |
| sentences: |
| - Je suis passionné par mes missions |
| - Très élevé, épuisé |
| - Je suis bien dans mon poste mais les perspectives d'évolution manquent |
| - source_sentence: Franchement, incertain, à voir selon l'évolution de l'entreprise |
| sentences: |
| - Je suis serein, aucune pression excessive |
| - Honnêtement, je ne sais pas encore, peut-être rester ou explorer |
| - Franchement, je me vois évoluer ici, obtenir une promotion |
| - source_sentence: Franchement, je construis mon avenir ici, pas question de partir |
| sentences: |
| - Je suis mécontent de la façon dont je suis traité |
| - Je passe des entretiens pour explorer d'autres opportunités |
| - Je suis fidèle à cette entreprise, elle me correspond |
| - source_sentence: Correct, sans plus |
| sentences: |
| - Franchement, bien, pas de pression |
| - Franchement, les valeurs correspondent à peu près |
| - Franchement, pas du tout motivé |
| - source_sentence: Franchement, je ne cherche pas du tout à partir, je suis épanoui |
| ici |
| sentences: |
| - Cette entreprise est une étape, pas une destination finale |
| - Cette expérience est temporaire dans mon parcours |
| - Franchement, les valeurs de l'entreprise correspondent aux miennes |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - pearson_cosine |
| - spearman_cosine |
| model-index: |
| - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: val rh |
| type: val_rh |
| metrics: |
| - type: pearson_cosine |
| value: 0.995510812995037 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.9788444539668885 |
| name: Spearman Cosine |
| --- |
| |
| # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision e8f8c211226b894fcb81acc59f3b34ba3efd5f42 --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 384 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Supported Modality:** Text |
| <!-- - **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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'}) |
| (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', '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 = [ |
| 'Franchement, je ne cherche pas du tout à partir, je suis épanoui ici', |
| 'Cette expérience est temporaire dans mon parcours', |
| "Franchement, les valeurs de l'entreprise correspondent aux miennes", |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 384] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.2799, 0.9658], |
| # [0.2799, 1.0000, 0.3024], |
| # [0.9658, 0.3024, 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: `val_rh` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | pearson_cosine | 0.9955 | |
| | **spearman_cosine** | **0.9788** | |
| |
| <!-- |
| ## 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: 4,500 training samples |
| * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
| * Approximate statistics based on the first 100 samples: |
| | | sentence_0 | sentence_1 | label | |
| |:---------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------| |
| | type | string | string | float | |
| | modality | text | text | | |
| | details | <ul><li>min: 8 tokens</li><li>mean: 15.15 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.33 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 0.13</li><li>mean: 0.62</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | label | |
| |:-------------------------------------------------------------------------|:----------------------------------------------------------------------------|:---------------------------------| |
| | <code>Honnêtement, je postule à d'autres offres régulièrement</code> | <code>Je veux partir le plus tôt possible</code> | <code>0.96</code> | |
| | <code>Honnêtement, mon avenir est ici, je ne cherche pas ailleurs</code> | <code>Je suis ici pour apprendre puis je verrai la suite ailleurs</code> | <code>0.35</code> | |
| | <code>Honnêtement, je suis très satisfait de mon poste</code> | <code>Franchement, je suis mécontent de la façon dont je suis traité</code> | <code>0.17999999999999994</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", |
| "cos_score_transformation": "torch.nn.modules.linear.Identity" |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `num_train_epochs`: 10 |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `do_predict`: False |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `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`: 10 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_ratio`: None |
| - `warmup_steps`: 0 |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `enable_jit_checkpoint`: False |
| - `save_on_each_node`: False |
| - `save_only_model`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `use_cpu`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `bf16`: False |
| - `fp16`: False |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `local_rank`: -1 |
| - `ddp_backend`: None |
| - `debug`: [] |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 0 |
| - `dataloader_prefetch_factor`: None |
| - `disable_tqdm`: False |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `load_best_model_at_end`: False |
| - `ignore_data_skip`: False |
| - `fsdp`: [] |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `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 |
| - `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 |
| - `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_for_metrics`: [] |
| - `eval_do_concat_batches`: True |
| - `auto_find_batch_size`: False |
| - `full_determinism`: False |
| - `ddp_timeout`: 1800 |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `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 |
| - `use_cache`: False |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: round_robin |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | val_rh_spearman_cosine | |
| |:------:|:----:|:-------------:|:----------------------:| |
| | 0.7092 | 200 | - | 0.9140 | |
| | 1.0 | 282 | - | 0.9358 | |
| | 1.4184 | 400 | - | 0.9261 | |
| | 1.7730 | 500 | 0.0233 | - | |
| | 2.0 | 564 | - | 0.9569 | |
| | 2.1277 | 600 | - | 0.9572 | |
| | 2.8369 | 800 | - | 0.9600 | |
| | 3.0 | 846 | - | 0.9636 | |
| | 3.5461 | 1000 | 0.0020 | 0.9642 | |
| | 4.0 | 1128 | - | 0.9698 | |
| | 4.2553 | 1200 | - | 0.9690 | |
| | 4.9645 | 1400 | - | 0.9738 | |
| | 5.0 | 1410 | - | 0.9736 | |
| | 5.3191 | 1500 | 0.0013 | - | |
| | 5.6738 | 1600 | - | 0.9717 | |
| | 6.0 | 1692 | - | 0.9723 | |
| | 6.3830 | 1800 | - | 0.9733 | |
| | 7.0 | 1974 | - | 0.9766 | |
| | 7.0922 | 2000 | 0.0010 | 0.9764 | |
| | 7.8014 | 2200 | - | 0.9781 | |
| | 8.0 | 2256 | - | 0.9774 | |
| | 8.5106 | 2400 | - | 0.9776 | |
| | 8.8652 | 2500 | 0.0008 | - | |
| | 9.0 | 2538 | - | 0.9781 | |
| | 9.2199 | 2600 | - | 0.9785 | |
| | 9.9291 | 2800 | - | 0.9788 | |
| | 10.0 | 2820 | - | 0.9788 | |
| |
| |
| ### Training Time |
| - **Training**: 6.6 minutes |
| |
| ### Framework Versions |
| - Python: 3.12.13 |
| - Sentence Transformers: 5.5.1 |
| - Transformers: 5.0.0 |
| - PyTorch: 2.11.0+cu128 |
| - Accelerate: 1.13.0 |
| - Datasets: 4.0.0 |
| - 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", |
| } |
| ``` |
| |
| <!-- |
| ## 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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