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 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': 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("LeoChiuu/all-MiniLM-L6-v2-negations")
# Run inference
sentences = [
'He published a history of Cornwall, New York in 1873.',
'He failed to publish a history of Cornwall, New York in 1873.',
"Salafis assert that reliance on taqlid has led to Islam 's decline.",
]
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]
Training Details
Training Dataset
Unnamed Dataset
- Size: 77,376 training samples
- Columns:
sentence_0
,sentence_1
, andlabel
- Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label type string string int details - min: 6 tokens
- mean: 16.2 tokens
- max: 57 tokens
- min: 5 tokens
- mean: 16.32 tokens
- max: 56 tokens
- 0: ~53.20%
- 1: ~46.80%
- Samples:
sentence_0 sentence_1 label The situation in Yemen was already much better than it was in Bahrain.
The situation in Yemen was not much better than Bahrain.
0
She was a member of the Gamma Theta Upsilon honour society of geography.
She was denied membership of the Gamma Theta Upsilon honour society of mathematics.
0
Which aren't small and not worth the price.
Which are small and not worth the price.
0
- Loss:
CosineSimilarityLoss
with these parameters:{ "loss_fct": "torch.nn.modules.loss.MSELoss" }
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size
: 16per_device_eval_batch_size
: 16num_train_epochs
: 10multi_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseprediction_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
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 10max_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
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_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, '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_sampler
: batch_samplermulti_dataset_batch_sampler
: round_robin
Training Logs
Epoch | Step | Training Loss |
---|---|---|
0.1034 | 500 | 0.3382 |
0.2068 | 1000 | 0.2112 |
0.3102 | 1500 | 0.1649 |
0.4136 | 2000 | 0.1454 |
0.5170 | 2500 | 0.1244 |
0.6203 | 3000 | 0.1081 |
0.7237 | 3500 | 0.0962 |
0.8271 | 4000 | 0.0924 |
0.9305 | 4500 | 0.0852 |
1.0339 | 5000 | 0.0812 |
1.1373 | 5500 | 0.0833 |
1.2407 | 6000 | 0.0736 |
1.3441 | 6500 | 0.0756 |
1.4475 | 7000 | 0.0665 |
1.5509 | 7500 | 0.0661 |
1.6543 | 8000 | 0.0625 |
1.7577 | 8500 | 0.0621 |
1.8610 | 9000 | 0.0593 |
1.9644 | 9500 | 0.054 |
2.0678 | 10000 | 0.0569 |
2.1712 | 10500 | 0.0566 |
2.2746 | 11000 | 0.0502 |
2.3780 | 11500 | 0.0516 |
2.4814 | 12000 | 0.0455 |
2.5848 | 12500 | 0.0454 |
2.6882 | 13000 | 0.0424 |
2.7916 | 13500 | 0.044 |
2.8950 | 14000 | 0.0376 |
2.9983 | 14500 | 0.0386 |
3.1017 | 15000 | 0.0392 |
3.2051 | 15500 | 0.0344 |
3.3085 | 16000 | 0.0348 |
3.4119 | 16500 | 0.0343 |
3.5153 | 17000 | 0.0322 |
3.6187 | 17500 | 0.0324 |
3.7221 | 18000 | 0.0278 |
3.8255 | 18500 | 0.0294 |
3.9289 | 19000 | 0.0292 |
4.0323 | 19500 | 0.0276 |
4.1356 | 20000 | 0.0285 |
4.2390 | 20500 | 0.026 |
4.3424 | 21000 | 0.0271 |
4.4458 | 21500 | 0.0248 |
4.5492 | 22000 | 0.0245 |
4.6526 | 22500 | 0.0253 |
4.7560 | 23000 | 0.022 |
4.8594 | 23500 | 0.0219 |
4.9628 | 24000 | 0.0207 |
5.0662 | 24500 | 0.0212 |
5.1696 | 25000 | 0.0218 |
5.2730 | 25500 | 0.0192 |
5.3763 | 26000 | 0.0198 |
5.4797 | 26500 | 0.0183 |
5.5831 | 27000 | 0.02 |
5.6865 | 27500 | 0.0176 |
5.7899 | 28000 | 0.0184 |
5.8933 | 28500 | 0.0157 |
5.9967 | 29000 | 0.0175 |
6.1001 | 29500 | 0.0175 |
6.2035 | 30000 | 0.0163 |
6.3069 | 30500 | 0.0173 |
6.4103 | 31000 | 0.0165 |
6.5136 | 31500 | 0.0152 |
6.6170 | 32000 | 0.0155 |
6.7204 | 32500 | 0.0132 |
6.8238 | 33000 | 0.0147 |
6.9272 | 33500 | 0.0145 |
7.0306 | 34000 | 0.014 |
7.1340 | 34500 | 0.0147 |
7.2374 | 35000 | 0.0126 |
7.3408 | 35500 | 0.0141 |
7.4442 | 36000 | 0.0127 |
7.5476 | 36500 | 0.0132 |
7.6510 | 37000 | 0.0125 |
7.7543 | 37500 | 0.0111 |
7.8577 | 38000 | 0.011 |
7.9611 | 38500 | 0.0125 |
8.0645 | 39000 | 0.0128 |
8.1679 | 39500 | 0.013 |
8.2713 | 40000 | 0.0115 |
8.3747 | 40500 | 0.0111 |
8.4781 | 41000 | 0.0108 |
8.5815 | 41500 | 0.012 |
8.6849 | 42000 | 0.0108 |
8.7883 | 42500 | 0.0105 |
8.8916 | 43000 | 0.0092 |
8.9950 | 43500 | 0.0115 |
9.0984 | 44000 | 0.0112 |
9.2018 | 44500 | 0.0096 |
9.3052 | 45000 | 0.0106 |
9.4086 | 45500 | 0.011 |
9.5120 | 46000 | 0.01 |
9.6154 | 46500 | 0.011 |
9.7188 | 47000 | 0.0097 |
9.8222 | 47500 | 0.0096 |
9.9256 | 48000 | 0.0102 |
Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.0.1
- Transformers: 4.40.2
- PyTorch: 2.3.0+cpu
- Accelerate: 0.32.1
- Datasets: 2.19.1
- 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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