Albertdebeauvais/all-MiniLM-L6-v2_bibliographie

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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/all-MiniLM-L6-v2
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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:

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'CARERI, Maria, FÉRY-HUE, Françoise (éd.) (2001), Album de manuscrits français du XIIIe siècle : mise en page et mise en texte, Roma, Viella',
    'F. FÉRY-HUE, M. CARERI, Album De Manuscrits Français Du Xiiie Siècle : Mise En Page Et Mise En Texte, (2001)',
    'GORMAN, Michael Murray (1983), "A Carolingian Epitome of St. Augustine\'s De Genesi ad litteram", Revue des études augustiniennes, 29, n° 1-2, p. 137-144 (réimpr. dans GORMAN, Michael Murray, Biblical commentaries from the early Middle Ages, Firenze, SISMEL, p. 41-48)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Evaluation

Metrics

Binary Classification

• Datasets: eval and test
• Evaluated with BinaryClassificationEvaluator

Metric	eval	test
cosine_accuracy	1.0	0.99
cosine_accuracy_threshold	0.5337	0.8199
cosine_f1	1.0	0.9912
cosine_f1_threshold	0.5337	0.4524
cosine_precision	1.0	0.9825
cosine_recall	1.0	1.0
cosine_ap	1.0	0.9997
cosine_mcc	1.0	0.9799

Training Details

Training Dataset

Unnamed Dataset

• Size: 10,750 training samples
• Columns: text1, text2, and label
• Approximate statistics based on the first 1000 samples:

  	text1	text2	label
  type	string	string	int
  details	min: 24 tokens mean: 71.15 tokens max: 173 tokens	min: 9 tokens mean: 60.14 tokens max: 169 tokens	0: ~45.90% 1: ~54.10%

• Samples:

  text1	text2	label
  TSUJI, Sahoko G. (1982), "Analyse iconographique de quelques miniatures des rouleaux d'Exultet dans leurs rapports avec le texte", Studia artium orientalis et occidentalis, 1, p. 15-28	Sahoko G. TSUJI, (2019), "Analyse iconographique de quelques miniatures des rouleaux d'Exultet dans leurs rapports avec le texte", Studia artium orientalis et occidentalis, 16, p. 207-225	0
  Aesopi Fabulae, XIIIe s. ; ms. pris à San Daniele del Friuli (anc. cote : 147) ; LAFFITTE (Marie-Pierre), " La Bibliothèque nationale et les "conquêtes artistiques" de la Révolution et de l'Empire : Les manuscrits d'Italie (1796-1815) ", Bulletin du bibliophile, 1989, n° 2, p. 273-323. Mss.	LAFFITTE, Marie-Pierre (1989), "La Bibliothèque nationale et les "conquêtes artistiques" de la Révolution et de l'Empire : Les manuscrits d'Italie (1796-1815)", Bulletin du bibliophile, 2, p. 273-323	1
  GARNIER, François (1997), "Le pèlerinage de la vie humaine de Guillaume de Digulleville", Art sacré : les Cahiers de Rencontre avec le patrimoine religieux, 8 (Sur les pas des pèlerins), p. 101-111	GARNIER, François (1997), "Les fresques de Lutz-en-Dunois", Art sacré : les Cahiers de Rencontre avec le patrimoine religieux, 6 (Les églises rurales), p. 155-164	0

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 100 evaluation samples
• Columns: text1, text2, and label
• Approximate statistics based on the first 100 samples:

  	text1	text2	label
  type	string	string	int
  details	min: 26 tokens mean: 68.4 tokens max: 146 tokens	min: 16 tokens mean: 57.67 tokens max: 154 tokens	0: ~46.00% 1: ~54.00%

• Samples:

  text1	text2	label
  JACOB, Marie (2014), "Le recueil du 'Lyon d'or' de Châtellerault : un livre d'ingénieur inédit de la fin du XVe siècle (Solesmes, Abbaye Saint-Pierre, ms. 163)", dans RABEL, Claudia (éd.), Le manuscrit enluminé : études réunies en hommage à Patricia Stirnemann, Paris, Le Léopard d'or (Cahiers du Léopard d'or, 16), p. 101-121	RABEL, Claudia (éd.) (2014), Le manuscrit enluminé: études réunies en hommage à Patricia Stirnemann, Paris, Le Léopard d'or (Cahiers du Léopard d'or, 16)	0
  RICHARDS, John (2007), "Fouquet and the Trecento", Zeitschrift für Kunstgeschichte, 70, n° 4, p. 449-472	LEBIGUE, Jean-Baptiste (2007), Initiation aux manuscrits liturgiques, Orléans, Institut de recherche et d'histoire des textes (Ædilis, Publications pédagogiques, 6)	0
  BALBARINI, Chiara (2001-2002), "Liturgia e iconografia negli Antifonari di San Francesco a Pisa", dans ALIDORI BATTAGLIA, Laura (éd.), Cicli e immagini bibliche nella miniatura. Atti del VI Congresso di storia della Miniatura, Urbino, 3-6 ottobre 2002 (Rivista di storia della miniatura, 6-7), p. 181-190	WAGNER, Pierre-Édouard (2001-2002), "Un missel du XIVe siècle à l'usage du diocèse de Metz provenant du couvent des augustines de Saint-Jean de Bassel", Cahiers Élie Fleur, 21, p. 15-31	0

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 20
• per_device_eval_batch_size: 20
• num_train_epochs: 4

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 20
• per_device_eval_batch_size: 20
• 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: 4
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• 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: 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: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	eval_cosine_ap	test_cosine_ap
-1	-1	-	-	0.7004	-
0.9294	500	0.0843	-	-	-
1.0	538	-	0.0122	1.0000	-
1.8587	1000	0.0303	-	-	-
2.0	1076	-	0.0129	1.0000	-
2.7881	1500	0.0223	-	-	-
3.0	1614	-	0.0057	1.0000	-
3.7175	2000	0.0171	-	-	-
4.0	2152	-	0.0043	1.0000	-
-1	-1	-	-	-	0.9997

Framework Versions

• Python: 3.12.2
• Sentence Transformers: 4.1.0
• Transformers: 4.49.0
• PyTorch: 2.6.0+cpu
• Accelerate: 1.3.0
• Datasets: 3.3.1
• 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",
}