Nessrine9/finetuned-snli-MiniLM-L12-v2-100k-en-fr

SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L12-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-L12-v2
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 384 tokens
• 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': 128, '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("Nessrine9/finetuned-snli-MiniLM-L12-v2-100k-en-fr")
# Run inference
sentences = [
    "L' ancien n' est pas une classification juridique qui entraîne une perte automatique de ces droits .",
    'Ils voulaient plaider pour les personnes âgées .',
    "Les villes grecques d' Anatolie ont été exclues de l' appartenance à la Confédération Delian .",
]
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

Semantic Similarity

• Dataset: snli-dev
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.3542
spearman_cosine	0.3593
pearson_manhattan	0.3494
spearman_manhattan	0.3583
pearson_euclidean	0.3498
spearman_euclidean	0.3593
pearson_dot	0.3542
spearman_dot	0.3593
pearson_max	0.3542
spearman_max	0.3593

Training Details

Training Dataset

Unnamed Dataset

• Size: 100,000 training samples
• Columns: sentence_0, sentence_1, and label
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  details	min: 5 tokens mean: 34.31 tokens max: 128 tokens	min: 3 tokens mean: 18.24 tokens max: 51 tokens	min: 0.0 mean: 0.5 max: 1.0

• Samples:

  sentence_0	sentence_1	label
  We 're off ! "	We 're not headed off .	1.0
  Il y en a eu un ici récemment qui me vient à l' esprit que c' est à propos d' une femme que c' est ridicule je veux dire que c' est presque euh ce serait drôle si ce n' était pas si triste je veux dire cette femme cette femme est sortie et a engagé quelqu' un à	Cette femme a engagé quelqu' un récemment pour le faire et s' est fait prendre immédiatement .	0.5
  Gentilello a précisé qu' il n' avait pas critiqué le processus d' examen par les pairs , mais que les panels qui examinent les interventions en matière d' alcool dans l' eds devraient inclure des représentants de la médecine d' urgence .	Gentilello S' est ensuite battu avec un psychiatre sur le parking .	0.5

• Loss: CosineSimilarityLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 4
• fp16: True
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• 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: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• 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: 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: {'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: 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
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	snli-dev_spearman_max
0.08	500	0.1948	0.0484
0.16	1000	0.1752	0.1177
0.24	1500	0.1727	0.1136
0.32	2000	0.1668	0.2050
0.4	2500	0.1673	0.2227
0.48	3000	0.1651	0.1760
0.56	3500	0.1619	0.2195
0.64	4000	0.1625	0.2308
0.72	4500	0.1563	0.2405
0.8	5000	0.1598	0.2773
0.88	5500	0.1589	0.2359
0.96	6000	0.1587	0.2084
1.0	6250	-	0.2615
1.04	6500	0.158	0.2958
1.12	7000	0.1557	0.2887
1.2	7500	0.1544	0.2960
1.28	8000	0.1535	0.2977
1.3600	8500	0.1559	0.2546
1.44	9000	0.1518	0.3201
1.52	9500	0.1551	0.2894
1.6	10000	0.149	0.2981
1.6800	10500	0.152	0.3140
1.76	11000	0.1484	0.3056
1.8400	11500	0.1497	0.3051
1.92	12000	0.1522	0.2893
2.0	12500	0.1503	0.2944
2.08	13000	0.1496	0.3039
2.16	13500	0.1462	0.3314
2.24	14000	0.1505	0.2470
2.32	14500	0.1457	0.3081
2.4	15000	0.1478	0.3204
2.48	15500	0.1464	0.3248
2.56	16000	0.1442	0.3360
2.64	16500	0.1437	0.3418
2.7200	17000	0.1416	0.3496
2.8	17500	0.1434	0.3283
2.88	18000	0.146	0.3246
2.96	18500	0.1448	0.3352
3.0	18750	-	0.3248
3.04	19000	0.1445	0.3394
3.12	19500	0.1423	0.3430
3.2	20000	0.1415	0.3410
3.2800	20500	0.1411	0.3367
3.36	21000	0.1445	0.3497
3.44	21500	0.1383	0.3640
3.52	22000	0.1408	0.3497
3.6	22500	0.1374	0.3452
3.68	23000	0.1401	0.3519
3.76	23500	0.137	0.3582
3.84	24000	0.1393	0.3610
3.92	24500	0.1408	0.3575
4.0	25000	0.1388	0.3593

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.2.1
• Transformers: 4.44.2
• PyTorch: 2.5.0+cu121
• Accelerate: 0.34.2
• Datasets: 3.0.2
• Tokenizers: 0.19.1

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",
}