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

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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.

python

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

Datasets: all-nli-dev and all-nli-test
Evaluated with TripletEvaluator

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:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

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:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

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

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}
}

	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