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/finetuned2-snli-MiniLM-L12-v2")
# Run inference
sentences = [
'Many people outside on bicycles.',
'People are riding bikes in a race.',
'Protesters are in the back of a photo with a magazine display in the foreground.',
]
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 | 0.5041 |
spearman_cosine | 0.4962 |
pearson_manhattan | 0.4848 |
spearman_manhattan | 0.4957 |
pearson_euclidean | 0.4855 |
spearman_euclidean | 0.4962 |
pearson_dot | 0.5041 |
spearman_dot | 0.4962 |
pearson_max | 0.5041 |
spearman_max | 0.4962 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 100,000 training samples
- Columns:
sentence_0
,sentence_1
, andlabel
- Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label type string string float details - min: 7 tokens
- mean: 16.36 tokens
- max: 45 tokens
- min: 4 tokens
- mean: 10.62 tokens
- max: 33 tokens
- min: 0.0
- mean: 0.5
- max: 1.0
- Samples:
sentence_0 sentence_1 label A mother and two children opening gifts on a Christmas morning.
A mother and children cut into a large pizza.
1.0
Two men in protective gear are in a speed car racing to the finish line.
Two boys are playing tag.
1.0
A person in a pink jacket is running onto the field.
The woman is running on to the field.
0.5
- Loss:
CosineSimilarityLoss
with these parameters:{ "loss_fct": "torch.nn.modules.loss.MSELoss" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 16per_device_eval_batch_size
: 16num_train_epochs
: 4fp16
: Truemulti_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 4max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseeval_use_gather_object
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: round_robin
Training Logs
Epoch | Step | Training Loss | snli-dev_spearman_max |
---|---|---|---|
0.08 | 500 | 0.1782 | 0.3312 |
0.16 | 1000 | 0.1516 | 0.3393 |
0.24 | 1500 | 0.1422 | 0.3798 |
0.32 | 2000 | 0.1405 | 0.3675 |
0.4 | 2500 | 0.137 | 0.4029 |
0.48 | 3000 | 0.1398 | 0.3989 |
0.56 | 3500 | 0.136 | 0.4216 |
0.64 | 4000 | 0.1351 | 0.4322 |
0.72 | 4500 | 0.1317 | 0.4223 |
0.8 | 5000 | 0.1293 | 0.4331 |
0.88 | 5500 | 0.1318 | 0.4416 |
0.96 | 6000 | 0.1311 | 0.4185 |
1.0 | 6250 | - | 0.4522 |
1.04 | 6500 | 0.129 | 0.4312 |
1.12 | 7000 | 0.1272 | 0.4544 |
1.2 | 7500 | 0.1271 | 0.4533 |
1.28 | 8000 | 0.125 | 0.4456 |
1.3600 | 8500 | 0.1229 | 0.4570 |
1.44 | 9000 | 0.1241 | 0.4529 |
1.52 | 9500 | 0.1254 | 0.4517 |
1.6 | 10000 | 0.1232 | 0.4563 |
1.6800 | 10500 | 0.1232 | 0.4565 |
1.76 | 11000 | 0.1198 | 0.4521 |
1.8400 | 11500 | 0.1201 | 0.4570 |
1.92 | 12000 | 0.1238 | 0.4758 |
2.0 | 12500 | 0.1195 | 0.4671 |
2.08 | 13000 | 0.1155 | 0.4582 |
2.16 | 13500 | 0.1208 | 0.4787 |
2.24 | 14000 | 0.1164 | 0.4733 |
2.32 | 14500 | 0.1164 | 0.4743 |
2.4 | 15000 | 0.1136 | 0.4733 |
2.48 | 15500 | 0.1177 | 0.4704 |
2.56 | 16000 | 0.1152 | 0.4711 |
2.64 | 16500 | 0.1162 | 0.4827 |
2.7200 | 17000 | 0.1136 | 0.4772 |
2.8 | 17500 | 0.1129 | 0.4853 |
2.88 | 18000 | 0.1161 | 0.4830 |
2.96 | 18500 | 0.1144 | 0.4827 |
3.0 | 18750 | - | 0.4850 |
3.04 | 19000 | 0.112 | 0.4920 |
3.12 | 19500 | 0.1105 | 0.4901 |
3.2 | 20000 | 0.1122 | 0.4925 |
3.2800 | 20500 | 0.1114 | 0.4913 |
3.36 | 21000 | 0.1074 | 0.4887 |
3.44 | 21500 | 0.1093 | 0.4819 |
3.52 | 22000 | 0.1107 | 0.4853 |
3.6 | 22500 | 0.1088 | 0.4897 |
3.68 | 23000 | 0.1095 | 0.4922 |
3.76 | 23500 | 0.11 | 0.4923 |
3.84 | 24000 | 0.1075 | 0.4950 |
3.92 | 24500 | 0.1107 | 0.4967 |
4.0 | 25000 | 0.1073 | 0.4962 |
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",
}
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Model tree for cherifkhalifah/finetuned2-snli-MiniLM-L12-v2
Base model
sentence-transformers/all-MiniLM-L12-v2Evaluation results
- Pearson Cosine on snli devself-reported0.504
- Spearman Cosine on snli devself-reported0.496
- Pearson Manhattan on snli devself-reported0.485
- Spearman Manhattan on snli devself-reported0.496
- Pearson Euclidean on snli devself-reported0.485
- Spearman Euclidean on snli devself-reported0.496
- Pearson Dot on snli devself-reported0.504
- Spearman Dot on snli devself-reported0.496
- Pearson Max on snli devself-reported0.504
- Spearman Max on snli devself-reported0.496