--- 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) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity - **Supported Modality:** Text ### 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]]) ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `val_rh` * Evaluated with [EmbeddingSimilarityEvaluator](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** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 4,500 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 100 samples: | | sentence_0 | sentence_1 | label | |:---------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------| | type | string | string | float | | modality | text | text | | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:-------------------------------------------------------------------------|:----------------------------------------------------------------------------|:---------------------------------| | Honnêtement, je postule à d'autres offres régulièrement | Je veux partir le plus tôt possible | 0.96 | | Honnêtement, mon avenir est ici, je ne cherche pas ailleurs | Je suis ici pour apprendre puis je verrai la suite ailleurs | 0.35 | | Honnêtement, je suis très satisfait de mon poste | Franchement, je suis mécontent de la façon dont je suis traité | 0.17999999999999994 | * Loss: [CosineSimilarityLoss](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
Click to expand - `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`: {}
### 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", } ```