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 <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Datasets:
  • train
  • test

<!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

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]])

<!-- ### 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.* -->

Evaluation

Metrics

Binary Classification

• Dataset: eval
• Evaluated with <code>BinaryClassificationEvaluator</code>

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Training Details

Training Datasets

train

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

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

• Samples:

  text1	text2	label
  <code>FORONDA, François (dir.), BARRALIS, Christine (dir.), SÈRE, Bénédicte (dir.) (2010), Violences souveraines au Moyen Âge. Travaux d'une école historique, Paris (Le nœud gordien)</code>	<code>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</code>	<code>0</code>
  <code>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</code>	<code>TOSCANO, Gennaro (1995), "Il Maestro di Isabella di Chiaromonte : note sulla miniatura a Napoli a metà Quattracento", Artes, 3, p. 34-45</code>	<code>0</code>
  <code>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)</code>	<code>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</code>	<code>1</code>

• Loss: <code>OnlineContrastiveLoss</code>

test

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

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

• Samples:

  text1	text2	label
  <code>Pfeffer, Wendy, The Oxford Companion to Chaucer, Oxford, Oxford University Press, 2003.</code>	<code>Eglal Doss-Quinby, Joan Tasker-Grimbert, Wendy Pfeffer et Elizabeth Aubrey, Song of the Women Trouvères, New Haven/London, Yale University Press, 2001.</code>	<code>0</code>
  <code>Rêves et vie spirituelle d'après Evagre le Pontique, Jérusalem, Presses Universitaires de France, 1969.</code>	<code>(1969), « Les songes et la vie quotidienne dans l'Antiquité tardive », Jérusalem, éd. Presses Universitaires de France.</code>	<code>0</code>
  <code>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</code>	<code>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</code>	<code>1</code>

• Loss: <code>OnlineContrastiveLoss</code>

Evaluation Dataset

Unnamed Dataset

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

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

• Samples:

  text1	text2	label
  <code>Pfeffer, Wendy, The Oxford Companion to Chaucer, Oxford, Oxford University Press, 2003.</code>	<code>Eglal Doss-Quinby, Joan Tasker-Grimbert, Wendy Pfeffer et Elizabeth Aubrey, Song of the Women Trouvères, New Haven/London, Yale University Press, 2001.</code>	<code>0</code>
  <code>Rêves et vie spirituelle d'après Evagre le Pontique, Jérusalem, Presses Universitaires de France, 1969.</code>	<code>(1969), « Les songes et la vie quotidienne dans l'Antiquité tardive », Jérusalem, éd. Presses Universitaires de France.</code>	<code>0</code>
  <code>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</code>	<code>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</code>	<code>1</code>

• Loss: <code>OnlineContrastiveLoss</code>

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

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

• 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: {}

</details>

Training Logs

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",
}

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