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

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

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",
}
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