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gguichard
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matching-rh-peft2

Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
dense
Generated from Trainer
dataset_size:17380
loss:CosineSimilarityLoss
Model card Files
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SentenceTransformer based on EuroBERT/EuroBERT-210m

This is a sentence-transformers model finetuned from EuroBERT/EuroBERT-210m on the matching_rh_val10 dataset. 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: EuroBERT/EuroBERT-210m
  • Maximum Sequence Length: 8192 tokens
  • Output Dimensionality: 768 dimensions
  • Similarity Function: Cosine Similarity
  • Training Dataset:

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'EuroBertModel'})
  (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("gguichard/matching-rh-peft2")
# Run inference
sentences = [
    '{"type": "opportunity", "customer_code": "", "opportunity_title": ".NET Developer", "opportunity_place": "", "opportunity_expertise_area": "Autres", "opportunity_tools": "", "opportunity_activity_area": "", "opportunity_type": "1", "opportunity_description": ".NET\\nReact", "opportunity_criteria": "", "opportunity_extract": 1}',
    '{"type": "candidate", "customer_code": "", "title": "Agile Back end  Developer", "skills": "", "education": "", "experience": "-1", "tools": "", "languages": "", "mobility": "", "expertise_area": "", "activity_area": "", "list_diplomes": "", "typeOf": "0", "source": "", "informationComments": "", "extract": 1, "experiences": "[]"}',
    '{"type": "candidate", "customer_code": "", "title": "Consultant Data", "skills": "", "education": "", "experience": "-1", "tools": "", "languages": "", "mobility": "mondeeuropefrancerhonealpes", "expertise_area": "", "activity_area": "", "list_diplomes": "", "typeOf": "-1", "source": "3", "informationComments": "pas à l\'écoute", "extract": 1, "experiences": "[]"}',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8589, 0.4015],
#         [0.8589, 1.0000, 0.4875],
#         [0.4015, 0.4875, 1.0000]])

Training Details

Training Dataset

matching_rh_val10

  • Dataset: matching_rh_val10 at 16fd0da
  • Size: 17,380 training samples
  • Columns: label, sentence1, and sentence2
  • Approximate statistics based on the first 1000 samples:
    label sentence1 sentence2
    type float string string
    details
    • min: 0.0
    • mean: 0.84
    • max: 1.0
    • min: 80 tokens
    • mean: 352.97 tokens
    • max: 3661 tokens
    • min: 90 tokens
    • mean: 615.01 tokens
    • max: 6579 tokens
  • Samples:
    label sentence1 sentence2
    1.0 {"type": "opportunity", "customer_code": "", "opportunity_title": "DATA MANAGER - La POSTE", "opportunity_place": "", "opportunity_expertise_area": "Services", "opportunity_tools": "", "opportunity_activity_area": "", "opportunity_type": "1", "opportunity_description": "", "opportunity_criteria": "", "opportunity_extract": 1} {"type": "candidate", "customer_code": "", "title": "Senior Consultant/Project Manager - Data Management", "skills": "", "education": "", "experience": "-1", "tools": "", "languages": "", "mobility": "", "expertise_area": "", "activity_area": "", "list_diplomes": "BACHELOR - Mathématiques Appliquées - stratégique Université Paris I Panthéon Sorbonne, DEUG - Option Statistique - stratégique Université Paris I Panthéon Sorbonne", "typeOf": "-1", "source": "1", "informationComments": "adresse perso consultant : 99 rue Alfred DININ 92000 Nanterre", "extract": 1, "experiences": "[{'skills': '', 'startMonth': '', 'endDate': '', 'startYear': '', 'description': "Avril ❖Mission : * Automatisation et fiabilisation des calculs de l'inventaire de réassurance sur les produits de prévoyance individuelle commercialisés par les partenaires d'Axa France (SAS/SQL) * Etude de l'efficience et de la rentabilité des traités de réassurance mis en place pour sécuriser le portefeuille de ces produits (SAS/C++...
    1.0 {"type": "opportunity", "customer_code": "", "opportunity_title": "BABILOU - Responsable infra", "opportunity_place": "", "opportunity_expertise_area": "Autres", "opportunity_tools": "", "opportunity_activity_area": "", "opportunity_type": "1", "opportunity_description": "", "opportunity_criteria": "", "opportunity_extract": 1} {"type": "candidate", "customer_code": "", "title": "CHEF DE PROJET INFRASTRUCTURE", "skills": "", "education": "", "experience": "-1", "tools": "", "languages": "", "mobility": "", "expertise_area": "", "activity_area": "", "list_diplomes": "2020 - Microsoft Azure Artificial Intelligence - Microsoft Azure Fundamentals, 2014 - DEA - Probabilités et Applications - Université, 2003 - Diplôme d'ingénieur - Télécoms ENST ParisTech, 2003 - DEA - Signal et Communications Numériques - Université de Nice Sophia-Antipolis", "typeOf": "-1", "source": "1", "informationComments": "", "extract": 1, "experiences": "[{'skills': '', 'startMonth': '', 'endDate': '', 'startYear': '', 'description': '23 mois Études, architecture, ingénierie et paramétrage des réseaux de signalisation et de transit', 'company': '', 'location': '', 'id': '1947', 'title': 'Ingénieur accès fixe et mobile - Contexte - 01/10/2005 - 01/08/2007', 'endMonth': '', 'endYear': '', 'startDate': ''}, {'skills': '', 'startMonth': '', '...
    1.0 {"type": "opportunity", "customer_code": "", "opportunity_title": "DGFIP - ONEPOINT - Consultant JCL", "opportunity_place": "", "opportunity_expertise_area": "Autres", "opportunity_tools": "", "opportunity_activity_area": "", "opportunity_type": "1", "opportunity_description": "", "opportunity_criteria": "", "opportunity_extract": 1} {"type": "candidate", "customer_code": "", "title": "analyste developpeur pacbase cobol db2", "skills": "cobol, pacbase, db2, cics", "education": "", "experience": "-1", "tools": "", "languages": "", "mobility": "mondeeuropefranceiledefranceparis, mondeeuropefranceiledefranceseineetmarne, mondeeuropefranceiledefranceyvelines, mondeeuropefranceiledefranceessone, mondeeuropefranceiledefrancehautsdeseine92, mondeeuropefranceiledefranceseinesaintdenis, mondeeuropefranceiledefrancevaldemarne, mondeeuropefranceiledefrancevaloise", "expertise_area": "", "activity_area": "", "list_diplomes": "", "typeOf": "0", "source": "", "informationComments": "Sabrina Kadrie\n06 83 65 01 64\nsabrina20@orange.fr", "extract": 1, "experiences": "[]"}
  • Loss: CosineSimilarityLoss with these parameters:
    {
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Evaluation Dataset

matching_rh_val10

  • Dataset: matching_rh_val10 at 16fd0da
  • Size: 17,380 evaluation samples
  • Columns: label, sentence1, and sentence2
  • Approximate statistics based on the first 1000 samples:
    label sentence1 sentence2
    type float string string
    details
    • min: 0.0
    • mean: 0.84
    • max: 1.0
    • min: 80 tokens
    • mean: 352.97 tokens
    • max: 3661 tokens
    • min: 90 tokens
    • mean: 615.01 tokens
    • max: 6579 tokens
  • Samples:
    label sentence1 sentence2
    1.0 {"type": "opportunity", "customer_code": "", "opportunity_title": "DATA MANAGER - La POSTE", "opportunity_place": "", "opportunity_expertise_area": "Services", "opportunity_tools": "", "opportunity_activity_area": "", "opportunity_type": "1", "opportunity_description": "", "opportunity_criteria": "", "opportunity_extract": 1} {"type": "candidate", "customer_code": "", "title": "Senior Consultant/Project Manager - Data Management", "skills": "", "education": "", "experience": "-1", "tools": "", "languages": "", "mobility": "", "expertise_area": "", "activity_area": "", "list_diplomes": "BACHELOR - Mathématiques Appliquées - stratégique Université Paris I Panthéon Sorbonne, DEUG - Option Statistique - stratégique Université Paris I Panthéon Sorbonne", "typeOf": "-1", "source": "1", "informationComments": "adresse perso consultant : 99 rue Alfred DININ 92000 Nanterre", "extract": 1, "experiences": "[{'skills': '', 'startMonth': '', 'endDate': '', 'startYear': '', 'description': "Avril ❖Mission : * Automatisation et fiabilisation des calculs de l'inventaire de réassurance sur les produits de prévoyance individuelle commercialisés par les partenaires d'Axa France (SAS/SQL) * Etude de l'efficience et de la rentabilité des traités de réassurance mis en place pour sécuriser le portefeuille de ces produits (SAS/C++...
    1.0 {"type": "opportunity", "customer_code": "", "opportunity_title": "BABILOU - Responsable infra", "opportunity_place": "", "opportunity_expertise_area": "Autres", "opportunity_tools": "", "opportunity_activity_area": "", "opportunity_type": "1", "opportunity_description": "", "opportunity_criteria": "", "opportunity_extract": 1} {"type": "candidate", "customer_code": "", "title": "CHEF DE PROJET INFRASTRUCTURE", "skills": "", "education": "", "experience": "-1", "tools": "", "languages": "", "mobility": "", "expertise_area": "", "activity_area": "", "list_diplomes": "2020 - Microsoft Azure Artificial Intelligence - Microsoft Azure Fundamentals, 2014 - DEA - Probabilités et Applications - Université, 2003 - Diplôme d'ingénieur - Télécoms ENST ParisTech, 2003 - DEA - Signal et Communications Numériques - Université de Nice Sophia-Antipolis", "typeOf": "-1", "source": "1", "informationComments": "", "extract": 1, "experiences": "[{'skills': '', 'startMonth': '', 'endDate': '', 'startYear': '', 'description': '23 mois Études, architecture, ingénierie et paramétrage des réseaux de signalisation et de transit', 'company': '', 'location': '', 'id': '1947', 'title': 'Ingénieur accès fixe et mobile - Contexte - 01/10/2005 - 01/08/2007', 'endMonth': '', 'endYear': '', 'startDate': ''}, {'skills': '', 'startMonth': '', '...
    1.0 {"type": "opportunity", "customer_code": "", "opportunity_title": "DGFIP - ONEPOINT - Consultant JCL", "opportunity_place": "", "opportunity_expertise_area": "Autres", "opportunity_tools": "", "opportunity_activity_area": "", "opportunity_type": "1", "opportunity_description": "", "opportunity_criteria": "", "opportunity_extract": 1} {"type": "candidate", "customer_code": "", "title": "analyste developpeur pacbase cobol db2", "skills": "cobol, pacbase, db2, cics", "education": "", "experience": "-1", "tools": "", "languages": "", "mobility": "mondeeuropefranceiledefranceparis, mondeeuropefranceiledefranceseineetmarne, mondeeuropefranceiledefranceyvelines, mondeeuropefranceiledefranceessone, mondeeuropefranceiledefrancehautsdeseine92, mondeeuropefranceiledefranceseinesaintdenis, mondeeuropefranceiledefrancevaldemarne, mondeeuropefranceiledefrancevaloise", "expertise_area": "", "activity_area": "", "list_diplomes": "", "typeOf": "0", "source": "", "informationComments": "Sabrina Kadrie\n06 83 65 01 64\nsabrina20@orange.fr", "extract": 1, "experiences": "[]"}
  • Loss: CosineSimilarityLoss with these parameters:
    {
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 4
  • per_device_eval_batch_size: 4
  • learning_rate: 2e-05
  • num_train_epochs: 1
  • warmup_ratio: 0.1
  • log_level: error
  • log_level_replica: passive
  • log_on_each_node: False
  • logging_nan_inf_filter: False
  • bf16: True

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 4
  • per_device_eval_batch_size: 4
  • 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: 2e-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: error
  • log_level_replica: passive
  • log_on_each_node: False
  • logging_nan_inf_filter: False
  • 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}
  • parallelism_config: None
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch_fused
  • 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
  • hub_revision: None
  • 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
  • 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
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Epoch Step Training Loss Validation Loss
0.1151 500 0.1969 -
0.2301 1000 0.1491 0.1338
0.3452 1500 0.135 -
0.4603 2000 0.1247 0.1084
0.5754 2500 0.1137 -
0.6904 3000 0.122 0.0949
0.8055 3500 0.1089 -
0.9206 4000 0.1117 0.0879

Framework Versions

  • Python: 3.10.16
  • Sentence Transformers: 5.1.1
  • Transformers: 4.56.2
  • PyTorch: 2.8.0+cu128
  • Accelerate: 1.10.1
  • Datasets: 4.1.1
  • Tokenizers: 0.22.1

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",
}
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