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Godefroyduchalard/solone-embedding-final1

sourceHugging Faceupdated 2y agoView on Hugging Face

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Model Card

SentenceTransformer based on OrdalieTech/Solon-embeddings-large-0.1

This is a sentence-transformers model finetuned from OrdalieTech/Solon-embeddings-large-0.1. It maps sentences & paragraphs to a 1024-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: OrdalieTech/Solon-embeddings-large-0.1
—Maximum Sequence Length: 512 tokens
—Output Dimensionality: 1024 dimensions
—Similarity Function: Cosine Similarity

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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

bash

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("Godefroyduchalard/solone-embedding-final1")
# Run inference
sentences = [
    'aléa',
    'Événement imprévu et incertain qui peut avoir un impact sur une situation ou une décision',
    "L'aléa désigne un événement prévu et certain qui est intégré dans une stratégie de gestion des risques pour garantir le succès d'une décision administrative.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

Unnamed Dataset

—Size: 66 training samples
—Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
—Approximate statistics based on the first 66 samples: | | anchor | positive | negative | |:--------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 4.18 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.47 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 35.7 tokens</li><li>max: 61 tokens</li></ul> |
—Samples: | anchor | positive | negative | |:---------------------|:-----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Odeadom</code> | <code>Office de développement de l'économie agricole des départements d'outre-mer</code> | <code>L'Office d'Eradication des Déchets Agricoles dans les Départements Métropolitains.</code> | | <code>OFII</code> | <code>Office français de l'immigration et de l'intégration</code> | <code>L'Office français de l'immigration et de l'intégration est un organisme chargé de faciliter les déplacements internationaux des entreprises françaises à travers le monde.</code> | | <code>Ofpra</code> | <code>Office français de protection des réfugiés et apatrides</code> | <code>L'Ofpra est un organisme chargé de l'évaluation et du contrôle des demandes d'asile présentées par les étrangers qui souhaitent s'installer en France, tout en veillant à ce que ces derniers ne représentent pas une menace pour la sécurité nationale.</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }

Evaluation Dataset

Unnamed Dataset

—Size: 100 evaluation samples
—Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
—Approximate statistics based on the first 100 samples: | | anchor | positive | negative | |:--------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 4.62 tokens</li><li>max: 8 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.22 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 38.54 tokens</li><li>max: 61 tokens</li></ul> |
—Samples: | anchor | positive | negative | |:-----------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>discriminatoire</code> | <code>Se dit d'une action ou d'une décision qui traite une personne ou un groupe de manière injuste en se basant sur des critères tels que l'âge, le genre, l'origine ethnique, etc.</code> | <code>Le terme "discriminatoire" désigne une action ou décision qui favorise et protège les droits des personnes ou groupes marginalisés, en particulier dans le contexte de la protection de l'environnement.</code> | | <code>criminelle</code> | <code>Qui se rapporte à un crime ou à son exécution.</code> | <code>Qui est responsable d'assurer le respect des lois et règlements dans un contexte sportif ou ludique.</code> | | <code>liquidité</code> | <code>La liquidité est la facilité avec laquelle un actif peut être converti en espèces ou en équivalent liquide sans perdre de valeur.</code> | <code>La liquidité est le processus de détermination des coûts indirects d'une entreprise, qui consiste à évaluer les dépenses non directement liées aux produits ou services offerts.</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }

Training Hyperparameters

Non-Default Hyperparameters

—eval_strategy: steps
—per_device_train_batch_size: 16
—per_device_eval_batch_size: 16
—learning_rate: 0.1
—num_train_epochs: 10
—warmup_ratio: 0.1
—fp16: True
—batch_sampler: no_duplicates

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: 16
—per_device_eval_batch_size: 16
—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: 0.1
—weight_decay: 0.0
—adam_beta1: 0.9
—adam_beta2: 0.999
—adam_epsilon: 1e-08
—max_grad_norm: 1.0
—num_train_epochs: 10
—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: False
—fp16: True
—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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: False
—hub_always_push: False
—gradient_checkpointing: False
—gradient_checkpointing_kwargs: None
—include_inputs_for_metrics: False
—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
—eval_use_gather_object: False
—prompts: None
—batch_sampler: no_duplicates
—multi_dataset_batch_sampler: proportional

</details>

Training Logs

Epoch	Step	Training Loss	Validation Loss
1.0	5	3.4687	3.3345
2.0	10	3.1421	nan
3.0	15	0.0	nan
4.0	20	0.0	nan
5.0	25	0.0	nan
6.0	30	0.0	nan
7.0	35	0.0	nan
8.0	40	0.0	nan
9.0	45	0.0	nan
10.0	50	0.0	nan

Framework Versions

—Python: 3.11.9
—Sentence Transformers: 3.3.0
—Transformers: 4.44.0
—PyTorch: 2.4.1+cu121
—Accelerate: 1.0.0
—Datasets: 2.20.0
—Tokenizers: 0.19.1

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}
}

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