cherifkhalifah/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("cherifkhalifah/finetuned-snli-MiniLM-L12-v2-100k-en-fr")
# Run inference
sentences = [
    "it looks fertile and it it um i mean it rains enough they have the climate and the rain and if not it 's like i 've been to Saint Thomas and it just starts from the ocean up",
    'They have the rain and the climate so I imagine the lands would be fertile .',
    "They don 't know how to do it .",
]
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	0.3725
spearman_cosine	0.3729
pearson_manhattan	0.365
spearman_manhattan	0.3725
pearson_euclidean	0.3657
spearman_euclidean	0.3729
pearson_dot	0.3725
spearman_dot	0.3729
pearson_max	0.3725
spearman_max	0.3729

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: 35.27 tokens max: 128 tokens	min: 5 tokens mean: 18.46 tokens max: 66 tokens	min: 0.0 mean: 0.5 max: 1.0

• Samples:

  sentence_0	sentence_1	label
  Natalia M' a regardé .	Natalia a regardé et attend que je lui donne l' épée .	0.5
  And he sounded sincere .	He sounded sincere.He was sounding sincere in his words .	0.0
  There 's a small zoo area where you can see snakes , lizards , birds of prey , wolves , hyenas , foxes , and various desert cats , including cheetahs and leopards .	The zoo is home to some endangered desert animals .	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	0.2008	0.0433
0.16	1000	0.1757	0.1024
0.24	1500	0.1732	0.1503
0.32	2000	0.1685	0.2168
0.4	2500	0.1702	0.2206
0.48	3000	0.1676	0.2117
0.56	3500	0.1637	0.2624
0.64	4000	0.1636	0.2169
0.72	4500	0.1608	0.0051
0.8	5000	0.1601	0.2236
0.88	5500	0.1597	0.2471
0.96	6000	0.1596	0.2934
1.0	6250	-	0.2905
1.04	6500	0.1602	0.3001
1.12	7000	0.1571	0.3116
1.2	7500	0.1588	0.3145
1.28	8000	0.1562	0.3304
1.3600	8500	0.1548	0.3376
1.44	9000	0.156	0.3359
1.52	9500	0.1552	0.3194
1.6	10000	0.153	0.3474
1.6800	10500	0.1529	0.3220
1.76	11000	0.1518	0.3255
1.8400	11500	0.1499	0.3332
1.92	12000	0.1524	0.3521
2.0	12500	0.1512	0.3425
2.08	13000	0.1514	0.3462
2.16	13500	0.1516	0.3414
2.24	14000	0.1532	0.3453
2.32	14500	0.1459	0.3699
2.4	15000	0.1524	0.3576
2.48	15500	0.1506	0.3418
2.56	16000	0.1488	0.3559
2.64	16500	0.1486	0.3597
2.7200	17000	0.1469	0.3552
2.8	17500	0.1448	0.3459
2.88	18000	0.1458	0.3503
2.96	18500	0.1468	0.3647
3.0	18750	-	0.3611
3.04	19000	0.1472	0.3741
3.12	19500	0.1457	0.3603
3.2	20000	0.147	0.3576
3.2800	20500	0.1451	0.3663
3.36	21000	0.1438	0.3734
3.44	21500	0.1471	0.3698
3.52	22000	0.1462	0.3646
3.6	22500	0.1436	0.3740
3.68	23000	0.1441	0.3696
3.76	23500	0.1423	0.3636
3.84	24000	0.1411	0.3713
3.92	24500	0.1438	0.3706
4.0	25000	0.1421	0.3729

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.1.1
• Transformers: 4.44.2
• PyTorch: 2.4.1+cu121
• Accelerate: 0.34.2
• Datasets: 3.0.1
• 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",
}