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/finetuned2-snli-MiniLM-L12-v2")
# Run inference
sentences = [
'A female researcher looking through a microscope.',
'A woman is researching with a microscope.',
'A small girl is playing video games',
]
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 | 0.4899 |
spearman_cosine | 0.4779 |
pearson_manhattan | 0.4692 |
spearman_manhattan | 0.4775 |
pearson_euclidean | 0.4692 |
spearman_euclidean | 0.4779 |
pearson_dot | 0.4899 |
spearman_dot | 0.4779 |
pearson_max | 0.4899 |
spearman_max | 0.4779 |
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: 16.32 tokens
- max: 86 tokens
- min: 4 tokens
- mean: 10.46 tokens
- max: 32 tokens
- min: 0.0
- mean: 0.5
- max: 1.0
- Samples:
sentence_0 sentence_1 label A man wearing jeans and a t-shirt plays guitar for a smiling woman and child as they sit on a staircase near red and orange balloons.
A man is in jail.
1.0
A boy wearing blue short standing on the traffic signal pole.
The boy is carrying his school books.
0.5
Several people on a busy street or perhaps at a fair.
They are walkng.
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 | 0.1832 | 0.3114 |
0.16 | 1000 | 0.1489 | 0.3518 |
0.24 | 1500 | 0.1468 | 0.3697 |
0.32 | 2000 | 0.1411 | 0.3723 |
0.4 | 2500 | 0.14 | 0.4062 |
0.48 | 3000 | 0.1366 | 0.3923 |
0.56 | 3500 | 0.1379 | 0.4143 |
0.64 | 4000 | 0.1357 | 0.3928 |
0.72 | 4500 | 0.1331 | 0.4067 |
0.8 | 5000 | 0.1338 | 0.4293 |
0.88 | 5500 | 0.1294 | 0.4183 |
0.96 | 6000 | 0.1305 | 0.4402 |
1.0 | 6250 | - | 0.4454 |
1.04 | 6500 | 0.1303 | 0.4408 |
1.12 | 7000 | 0.1275 | 0.4416 |
1.2 | 7500 | 0.1285 | 0.4287 |
1.28 | 8000 | 0.125 | 0.4404 |
1.3600 | 8500 | 0.1253 | 0.4408 |
1.44 | 9000 | 0.1246 | 0.4293 |
1.52 | 9500 | 0.126 | 0.4535 |
1.6 | 10000 | 0.1257 | 0.4455 |
1.6800 | 10500 | 0.1264 | 0.4520 |
1.76 | 11000 | 0.1248 | 0.4526 |
1.8400 | 11500 | 0.1208 | 0.4631 |
1.92 | 12000 | 0.1236 | 0.4635 |
2.0 | 12500 | 0.1239 | 0.4573 |
2.08 | 13000 | 0.1209 | 0.4569 |
2.16 | 13500 | 0.1194 | 0.4642 |
2.24 | 14000 | 0.1206 | 0.4539 |
2.32 | 14500 | 0.117 | 0.4633 |
2.4 | 15000 | 0.1171 | 0.4657 |
2.48 | 15500 | 0.1181 | 0.4633 |
2.56 | 16000 | 0.1197 | 0.4552 |
2.64 | 16500 | 0.1182 | 0.4670 |
2.7200 | 17000 | 0.1155 | 0.4684 |
2.8 | 17500 | 0.1171 | 0.4640 |
2.88 | 18000 | 0.1139 | 0.4715 |
2.96 | 18500 | 0.1164 | 0.4769 |
3.0 | 18750 | - | 0.4709 |
3.04 | 19000 | 0.1151 | 0.4704 |
3.12 | 19500 | 0.1144 | 0.4759 |
3.2 | 20000 | 0.1121 | 0.4795 |
3.2800 | 20500 | 0.1104 | 0.4697 |
3.36 | 21000 | 0.1127 | 0.4763 |
3.44 | 21500 | 0.1115 | 0.4742 |
3.52 | 22000 | 0.1126 | 0.4697 |
3.6 | 22500 | 0.1123 | 0.4735 |
3.68 | 23000 | 0.1132 | 0.4750 |
3.76 | 23500 | 0.1127 | 0.4743 |
3.84 | 24000 | 0.1086 | 0.4752 |
3.92 | 24500 | 0.1107 | 0.4781 |
4.0 | 25000 | 0.1114 | 0.4779 |
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",
}
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Model tree for Nessrine9/finetuned2-snli-MiniLM-L12-v2
Base model
sentence-transformers/all-MiniLM-L12-v2Evaluation results
- Pearson Cosine on snli devself-reported0.490
- Spearman Cosine on snli devself-reported0.478
- Pearson Manhattan on snli devself-reported0.469
- Spearman Manhattan on snli devself-reported0.478
- Pearson Euclidean on snli devself-reported0.469
- Spearman Euclidean on snli devself-reported0.478
- Pearson Dot on snli devself-reported0.490
- Spearman Dot on snli devself-reported0.478
- Pearson Max on snli devself-reported0.490
- Spearman Max on snli devself-reported0.478