biblissima/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 on the train and test datasets. 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
• Training Datasets:
  • train
  • test

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, 'architecture': '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("Albertdebeauvais/all-MiniLM-L6-v2_bibliographie")
# Run inference
sentences = [
    'RITZ-GUILBERT, Anne (2018), "Les modèles du \'Bréviaire de Marie de Savoie\' par le Maître des \'Vitae Imperatorum\'", dans BORLÉE, Denise (éd.), TERRIER ALIFERIS, Laurence (éd.), Les modèles dans l\'art du Moyen Âge (XIIe-XVe siècles), Turnhout, Brepols (Répertoire iconographique de la littérature du Moyen Âge. Les études du RILMA, 10), p. 109-120',
    'GIL, Marc (2018), "Sources et circulation des modèles dans les arts figurés champenois, vers 1160-1180 : le cas de Notre-Dame-en-Vaux à Châlons-en-Champagne", dans BORLÉE, Denise (éd.), TERRIER ALIFERIS, Laurence (éd.), Les modèles dans l\'art du Moyen Âge (XIIe-XVe siècles), Turnhout, Brepols (Répertoire iconographique de la littérature du Moyen Âge. Les études du RILMA, 10), p. 179-192',
    "LEMAÎTRE, Jean-Loup (éd.) (2005), Un calendrier retrouvé : le calendrier des Heures de Saint-Pierre-du-Queyroix, Ussel, Musée du pays d'Ussel",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5206, 0.1709],
#         [0.5206, 1.0000, 0.2801],
#         [0.1709, 0.2801, 1.0000]])

Evaluation

Metrics

Binary Classification

• Dataset: eval
• Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.9517
cosine_accuracy_threshold	0.8323
cosine_f1	0.9499
cosine_f1_threshold	0.8267
cosine_precision	0.9391
cosine_recall	0.961
cosine_ap	0.9647
cosine_mcc	0.9036

Training Details

Training Datasets

train

• Dataset: train
• Size: 26,107 training samples
• Columns: text1, text2, and label
• Approximate statistics based on the first 1000 samples:

  	text1	text2	label
  type	string	string	int
  details	min: 5 tokens mean: 66.2 tokens max: 200 tokens	min: 4 tokens mean: 63.27 tokens max: 199 tokens	0: ~53.50% 1: ~46.50%

• Samples:

  text1	text2	label
  FORONDA, François (dir.), BARRALIS, Christine (dir.), SÈRE, Bénédicte (dir.) (2010), Violences souveraines au Moyen Âge. Travaux d'une école historique, Paris (Le nœud gordien)	FORONDA, François (2010), "Une image de la violence d'Etat française : la mort de Pierre Ier de Castille", dans FORONDA, François (dir.), BARRALIS, Christine (dir.), SÈRE, Bénédicte (dir.), Violences souveraines au Moyen Âge. Travaux d'une école historique, Paris (Le nœud gordien), p. 249-259	0
  ORTOLEVA, Vincenzo (1994), "La cosiddetta tradizione "epitomata della Mulomedicina" di Vegezio. Recensio deterior o tradizionz indiretta ?", Revue d'histoire des textes, 24, p. 271-274	TOSCANO, Gennaro (1995), "Il Maestro di Isabella di Chiaromonte : note sulla miniatura a Napoli a metà Quattracento", Artes, 3, p. 34-45	0
  pp. XIV, 259-262, 264-265 John LOWDEN, The Making of the Bibles moralisées. T. 2 : The Book of Ruth, University Park, The Pennsylvania State University, 2000 Mss. [ 4° Impr. 2422 (2)	LOWDEN, John (2000), The Making of the Bibles Moralisées : I. The manuscripts; II. The book of Ruth, University Park (PA), The Pennsylvania State University Press	1

• Loss: OnlineContrastiveLoss

test

• Dataset: test
• Size: 808 training samples
• Columns: text1, text2, and label
• Approximate statistics based on the first 808 samples:

  	text1	text2	label
  type	string	string	int
  details	min: 12 tokens mean: 67.31 tokens max: 195 tokens	min: 11 tokens mean: 63.32 tokens max: 195 tokens	0: ~52.35% 1: ~47.65%

• Samples:

  text1	text2	label
  Pfeffer, Wendy, The Oxford Companion to Chaucer, Oxford, Oxford University Press, 2003.	Eglal Doss-Quinby, Joan Tasker-Grimbert, Wendy Pfeffer et Elizabeth Aubrey, Song of the Women Trouvères, New Haven/London, Yale University Press, 2001.	0
  Rêves et vie spirituelle d'après Evagre le Pontique, Jérusalem, Presses Universitaires de France, 1969.	(1969), « Les songes et la vie quotidienne dans l'Antiquité tardive », Jérusalem, éd. Presses Universitaires de France.	0
  HUCHER, Eugène (éd.) (1875-1878), Le Saint Graal, ou Le Joseph d'Arimathie, première branche des romans de la Table ronde publié d'après des textes et des documents inédits, Le Mans, Ed. Monnoyer, 3 volumes	1875-1878, Le Saint Graal, Ou Le Joseph D'arimathie, Première Branche Des Romans De La Table Ronde Publié D'après Des Textes Et Des Documents Inédits	1

• Loss: OnlineContrastiveLoss

Evaluation Dataset

Unnamed Dataset

• Size: 808 evaluation samples
• Columns: text1, text2, and label
• Approximate statistics based on the first 808 samples:

  	text1	text2	label
  type	string	string	int
  details	min: 12 tokens mean: 67.31 tokens max: 195 tokens	min: 11 tokens mean: 63.32 tokens max: 195 tokens	0: ~52.35% 1: ~47.65%

• Samples:

  text1	text2	label
  Pfeffer, Wendy, The Oxford Companion to Chaucer, Oxford, Oxford University Press, 2003.	Eglal Doss-Quinby, Joan Tasker-Grimbert, Wendy Pfeffer et Elizabeth Aubrey, Song of the Women Trouvères, New Haven/London, Yale University Press, 2001.	0
  Rêves et vie spirituelle d'après Evagre le Pontique, Jérusalem, Presses Universitaires de France, 1969.	(1969), « Les songes et la vie quotidienne dans l'Antiquité tardive », Jérusalem, éd. Presses Universitaires de France.	0
  HUCHER, Eugène (éd.) (1875-1878), Le Saint Graal, ou Le Joseph d'Arimathie, première branche des romans de la Table ronde publié d'après des textes et des documents inédits, Le Mans, Ed. Monnoyer, 3 volumes	1875-1878, Le Saint Graal, Ou Le Joseph D'arimathie, Première Branche Des Romans De La Table Ronde Publié D'après Des Textes Et Des Documents Inédits	1

• Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 160
• per_device_eval_batch_size: 160
• learning_rate: 3e-05
• warmup_ratio: 0.03

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 160
• per_device_eval_batch_size: 160
• 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: 3e-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: 3
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.03
• 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}
• 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	eval_cosine_ap
-1	-1	-	-	0.5884
0.0353	6	7.9026	-	-
0.0706	12	7.7888	-	-
0.1059	18	7.0352	-	-
0.1412	24	6.3592	-	-
0.1765	30	5.8148	-	-
0.2118	36	4.9098	-	-
0.2471	42	5.1715	-	-
0.2824	48	4.0856	-	-
0.3176	54	4.3722	-	-
0.3529	60	4.0175	-	-
0.3882	66	3.9427	-	-
0.4235	72	3.4966	-	-
0.4588	78	3.5505	-	-
0.4941	84	3.2389	-	-
0.5294	90	3.5375	-	-
0.5647	96	3.0543	-	-
0.6	102	3.0486	-	-
0.6353	108	2.5424	-	-
0.6706	114	2.9492	-	-
0.7059	120	3.353	-	-
0.7412	126	2.7673	-	-
0.7765	132	2.9456	-	-
0.8118	138	2.3598	-	-
0.8471	144	2.5187	-	-
0.8824	150	2.2102	-	-
0.9176	156	2.675	-	-
0.9529	162	2.1735	-	-
0.9882	168	2.4117	-	-
1.0	170	-	2.0486	0.9545
1.0235	174	1.8135	-	-
1.0588	180	2.1022	-	-
1.0941	186	1.7459	-	-
1.1294	192	1.7129	-	-
1.1647	198	1.7023	-	-
1.2	204	1.8	-	-
1.2353	210	1.6906	-	-
1.2706	216	2.0856	-	-
1.3059	222	1.7216	-	-
1.3412	228	1.8287	-	-
1.3765	234	2.2071	-	-
1.4118	240	1.8617	-	-
1.4471	246	1.8148	-	-
1.4824	252	1.6976	-	-
1.5176	258	1.4774	-	-
1.5529	264	1.8896	-	-
1.5882	270	1.8389	-	-
1.6235	276	2.2744	-	-
1.6588	282	1.5614	-	-
1.6941	288	1.3118	-	-
1.7294	294	1.6211	-	-
1.7647	300	1.3294	-	-
1.8	306	2.2436	-	-
1.8353	312	1.6333	-	-
1.8706	318	1.6046	-	-
1.9059	324	1.5298	-	-
1.9412	330	1.7025	-	-
1.9765	336	1.4742	-	-
2.0	340	-	1.5898	0.9664
2.0118	342	1.5415	-	-
2.0471	348	1.1568	-	-
2.0824	354	1.3209	-	-
2.1176	360	1.2234	-	-
2.1529	366	1.7336	-	-
2.1882	372	1.382	-	-
2.2235	378	1.665	-	-
2.2588	384	1.2707	-	-
2.2941	390	1.1796	-	-
2.3294	396	1.6894	-	-
2.3647	402	1.06	-	-
2.4	408	1.0879	-	-
2.4353	414	1.2806	-	-
2.4706	420	1.6574	-	-
2.5059	426	1.5029	-	-
2.5412	432	1.3803	-	-
2.5765	438	1.2059	-	-
2.6118	444	1.7823	-	-
2.6471	450	1.2976	-	-
2.6824	456	1.6891	-	-
2.7176	462	0.9401	-	-
2.7529	468	1.1141	-	-
2.7882	474	1.1229	-	-
2.8235	480	1.137	-	-
2.8588	486	1.5186	-	-
2.8941	492	1.4301	-	-
2.9294	498	1.4644	-	-
2.9647	504	0.9985	-	-
3.0	510	0.6255	1.5778	0.9647

Framework Versions

• Python: 3.9.21
• Sentence Transformers: 5.1.0
• Transformers: 4.56.1
• PyTorch: 2.8.0+cu129
• Accelerate: 1.10.1
• Datasets: 4.1.0
• Tokenizers: 0.22.0

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",
}