MPNet base trained on AllNLI triplets

This is a sentence-transformers model finetuned from pyrac/rse_engagement_des_collaborateurs. It maps sentences & paragraphs to a 768-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: pyrac/rse_engagement_des_collaborateurs
  • Maximum Sequence Length: 128 tokens
  • Output Dimensionality: 768 dimensions
  • Similarity Function: Cosine Similarity
  • Language: en
  • License: apache-2.0

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("pyrac/rse_gestion_durable")
# Run inference
sentences = [
    'Petit plus pour le caractère refuge LPO de l’hotel.',
    "L'établissement met en place des protocoles de sécurité au travail qui garantissent un environnement sain pour tous",
    'Parking pratique avec un bon rapport qualité-prix.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Triplet

Metric all-nli-dev all-nli-test
cosine_accuracy 1.0 1.0

Training Details

Training Dataset

Unnamed Dataset

  • Size: 132,020 training samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchor positive negative
    type string string string
    details
    • min: 4 tokens
    • mean: 22.06 tokens
    • max: 90 tokens
    • min: 4 tokens
    • mean: 19.57 tokens
    • max: 81 tokens
    • min: 6 tokens
    • mean: 18.83 tokens
    • max: 31 tokens
  • Samples:
    anchor positive negative
    Engagement RSE palpable, mais trop de règles vertes imposées. Les informations sur leurs pratiques responsables sont quasi inexistantes. Cette chambre était extrêmement décevante, elle ne correspondait absolument pas à nos besoins.
    Je suis déçu qu'aucun label environnemental comme Clef verte ne soit visible dans cet hôtel La mise en avant de leurs pratiques éthiques est impressionnante. Accès mal indiqué et compliqué.
    Le bien-être des employés est clairement une priorité ici avec des pratiques conformes aux dispositions légales Ils ne sont pas aussi transparents qu'ils le prétendent. La chambre était trop vieille et usée, ça a gâché notre séjour.
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "cos_sim"
    }
    

Evaluation Dataset

Unnamed Dataset

  • Size: 16,502 evaluation samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchor positive negative
    type string string string
    details
    • min: 4 tokens
    • mean: 21.43 tokens
    • max: 90 tokens
    • min: 5 tokens
    • mean: 19.94 tokens
    • max: 90 tokens
    • min: 6 tokens
    • mean: 19.08 tokens
    • max: 31 tokens
  • Samples:
    anchor positive negative
    J'ai trouvé que cet hôtel avec le label Clef verte est un bel exemple d'engagement environnemental personnels non-formés et mal payés, sous-traitance à gogo Pas assez d'espace pour les manœuvres, surtout en heures de pointe.
    Je ne vois pas de résultats concrets de leur engagement écologique. L'hôtel manque de transparence sur ses engagements en RSE. On nous a placé dans une chambre qui ne correspondait vraiment pas à ce que l’on avait réservé.
    Les conditions de sécurité au travail sont irréprochables et l'environnement est sain pour les employés et les clients RSE exemplaire, mais règles environnementales oppressives. Vraiment déçu d’avoir eu cette chambre, ce n’était pas du tout ce qu’on s’attendait.
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "cos_sim"
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 64
  • per_device_eval_batch_size: 64
  • num_train_epochs: 1
  • warmup_ratio: 0.1
  • bf16: True
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 64
  • per_device_eval_batch_size: 64
  • 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: 1
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • 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}
  • 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
  • dispatch_batches: None
  • split_batches: 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: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Epoch Step Training Loss Validation Loss all-nli-dev_cosine_accuracy all-nli-test_cosine_accuracy
0.0485 100 4.2915 4.1299 1.0 -
0.0969 200 4.1578 4.1253 1.0 -
0.1454 300 4.1509 4.1237 1.0 -
0.1939 400 4.1465 4.1006 1.0 -
0.2424 500 4.1224 4.0881 1.0 -
0.2908 600 4.1065 4.0597 1.0 -
0.3393 700 4.0901 4.0488 1.0 -
0.3878 800 4.0862 4.0355 1.0 -
0.4363 900 4.0732 4.0352 1.0 -
0.4847 1000 4.0681 4.0271 1.0 -
0.5332 1100 4.0574 4.0270 1.0 -
0.5817 1200 4.0583 4.0235 1.0 -
0.6302 1300 4.0566 4.0180 1.0 -
0.6786 1400 4.048 4.0180 1.0 -
0.7271 1500 4.046 4.0105 1.0 -
0.7756 1600 4.0403 4.0128 1.0 -
0.8240 1700 4.0471 4.0084 1.0 -
0.8725 1800 4.0455 4.0082 1.0 -
0.9210 1900 4.0328 4.0051 1.0 -
0.9695 2000 4.0417 4.0033 1.0 -
-1 -1 - - - 1.0

Framework Versions

  • Python: 3.12.3
  • Sentence Transformers: 3.4.1
  • Transformers: 4.48.3
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.3.0
  • Datasets: 3.2.0
  • Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@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

@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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