MPNet base trained on AllNLI triplets
This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2. 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: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
- 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.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("pyrac/rse_engagement_des_collaborateurs")
# Run inference
sentences = [
'On aurait aimé plus d’implication de la part des employés, c’était moyen.',
'Le service était assez médiocre, mais pas désastreux non plus.',
"On a été forcé d’accepter cette chambre, ce n'était pas du tout ce qu’on avait demandé.",
]
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
andall-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
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 11 tokens
- mean: 18.6 tokens
- max: 29 tokens
- min: 10 tokens
- mean: 16.93 tokens
- max: 27 tokens
- min: 6 tokens
- mean: 18.87 tokens
- max: 31 tokens
- Samples:
anchor positive negative L’équipe était tellement sympa et réactive, c’était vraiment agréable.
Nous avons été agréablement surpris par l’attention portée aux détails par le personnel.
Très bon service de navette depuis le parking.
C’était un service classique, ni particulièrement bon ni mauvais.
Le sourire et la disponibilité des employés ont illuminé notre séjour.
Cette chambre nous a totalement déçus, c’était tout sauf confortable.
Le service était correct, rien de plus.
Les employés étaient lents et mal organisés, cela a beaucoup gêné notre séjour.
La sécurité est assurée avec un gardien présent.
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Evaluation Dataset
Unnamed Dataset
- Size: 16,502 evaluation samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 10 tokens
- mean: 18.49 tokens
- max: 29 tokens
- min: 10 tokens
- mean: 16.97 tokens
- max: 27 tokens
- min: 6 tokens
- mean: 18.86 tokens
- max: 31 tokens
- Samples:
anchor positive negative Les employés semblaient démotivés et peu impliqués dans leur travail.
L'équipe semblait désorganisée et peu concernée par les besoins des clients.
La chambre n'était pas adaptée à nos attentes, c’était frustrant.
Le service était correct, mais il manquait un peu de chaleur humaine.
Un service qui n’a pas marqué mais qui reste acceptable.
Une chambre que nous n’avions pas demandée, c'était une vraie déception.
Les employés semblaient désintéressés, ça a un peu gâché l’expérience.
Le service était fonctionnel, mais pas très personnalisé.
Stationnement facile et rapide, un plaisir.
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 64per_device_eval_batch_size
: 64num_train_epochs
: 1warmup_ratio
: 0.1bf16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 64per_device_eval_batch_size
: 64per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 1max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: no_duplicatesmulti_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 | 5.2586 | 4.1160 | 1.0 | - |
0.0969 | 200 | 4.1542 | 4.1071 | 1.0 | - |
0.1454 | 300 | 4.1483 | 4.1009 | 1.0 | - |
0.1939 | 400 | 4.1327 | 4.0772 | 1.0 | - |
0.2424 | 500 | 4.1122 | 4.0561 | 1.0 | - |
0.2908 | 600 | 4.1027 | 4.0457 | 1.0 | - |
0.3393 | 700 | 4.0877 | 4.0345 | 1.0 | - |
0.3878 | 800 | 4.0863 | 4.0216 | 1.0 | - |
0.4363 | 900 | 4.0785 | 4.0196 | 1.0 | - |
0.4847 | 1000 | 4.0661 | 4.0182 | 1.0 | - |
0.5332 | 1100 | 4.0637 | 4.0163 | 1.0 | - |
0.5817 | 1200 | 4.0606 | 4.0130 | 1.0 | - |
0.6302 | 1300 | 4.0601 | 4.0086 | 1.0 | - |
0.6786 | 1400 | 4.0516 | 4.0037 | 1.0 | - |
0.7271 | 1500 | 4.0472 | 4.0015 | 1.0 | - |
0.7756 | 1600 | 4.0465 | 4.0008 | 1.0 | - |
0.8240 | 1700 | 4.0421 | 4.0007 | 1.0 | - |
0.8725 | 1800 | 4.0463 | 3.9944 | 1.0 | - |
0.9210 | 1900 | 4.035 | 3.9919 | 1.0 | - |
0.9695 | 2000 | 4.0408 | 3.9909 | 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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Evaluation results
- Cosine Accuracy on all nli devself-reported1.000
- Cosine Accuracy on all nli testself-reported1.000