matching-rh-peft2

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 <!-- at revision 25797134448b4c66f39791922c88150a83e3052d -->
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • matching_rh_val10

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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': 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]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

matching_rh_val10

• Dataset: matching_rh_val10 at 16fd0da
• Size: 17,380 training samples
• Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
• Approximate statistics based on the first 1000 samples:

  	label	sentence1	sentence2
  type	float	string	string
  details	<ul><li>min: 0.0</li><li>mean: 0.84</li><li>max: 1.0</li></ul>	<ul><li>min: 80 tokens</li><li>mean: 352.97 tokens</li><li>max: 3661 tokens</li></ul>	<ul><li>min: 90 tokens</li><li>mean: 615.01 tokens</li><li>max: 6579 tokens</li></ul>

• Samples:

  label	sentence1	sentence2
  <code>1.0</code>	<code>{"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}</code>	<code>{"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++...</code>
  <code>1.0</code>	<code>{"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}</code>	<code>{"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': '', '...</code>
  <code>1.0</code>	<code>{"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}</code>	<code>{"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\[email protected]", "extract": 1, "experiences": "[]"}</code>

• Loss: <code>CosineSimilarityLoss</code> 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: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
• Approximate statistics based on the first 1000 samples:

  	label	sentence1	sentence2
  type	float	string	string
  details	<ul><li>min: 0.0</li><li>mean: 0.84</li><li>max: 1.0</li></ul>	<ul><li>min: 80 tokens</li><li>mean: 352.97 tokens</li><li>max: 3661 tokens</li></ul>	<ul><li>min: 90 tokens</li><li>mean: 615.01 tokens</li><li>max: 6579 tokens</li></ul>

• Samples:

  label	sentence1	sentence2
  <code>1.0</code>	<code>{"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}</code>	<code>{"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++...</code>
  <code>1.0</code>	<code>{"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}</code>	<code>{"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': '', '...</code>
  <code>1.0</code>	<code>{"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}</code>	<code>{"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\[email protected]", "extract": 1, "experiences": "[]"}</code>

• Loss: <code>CosineSimilarityLoss</code> 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

<details><summary>Click to expand</summary>

• 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: {}

</details>

Training Logs

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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