metadata
base_model: sentence-transformers/all-MiniLM-L6-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:20
- loss:CoSENTLoss
widget: []
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: validation
type: validation
metrics:
- type: pearson_cosine
value: 0.15590647163663807
name: Pearson Cosine
- type: spearman_cosine
value: 0.28867513459481287
name: Spearman Cosine
- type: pearson_manhattan
value: 0.20874094632850035
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.28867513459481287
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.21989747670451043
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.28867513459481287
name: Spearman Euclidean
- type: pearson_dot
value: 0.15590640231031966
name: Pearson Dot
- type: spearman_dot
value: 0.28867513459481287
name: Spearman Dot
- type: pearson_max
value: 0.21989747670451043
name: Pearson Max
- type: spearman_max
value: 0.28867513459481287
name: Spearman Max
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("sentence_transformers_model_id")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
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:
validation
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.1559 |
spearman_cosine | 0.2887 |
pearson_manhattan | 0.2087 |
spearman_manhattan | 0.2887 |
pearson_euclidean | 0.2199 |
spearman_euclidean | 0.2887 |
pearson_dot | 0.1559 |
spearman_dot | 0.2887 |
pearson_max | 0.2199 |
spearman_max | 0.2887 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 20 training samples
- Columns:
sentence1
,sentence2
, andscore
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score type string string int details - min: 4 tokens
- mean: 7.0 tokens
- max: 12 tokens
- min: 8 tokens
- mean: 18.2 tokens
- max: 31 tokens
- 0: ~5.00%
- 1: ~95.00%
- Samples:
sentence1 sentence2 score search for anti slip shoes
Mens Shower Shoes With Holes Dry Quickly Bath Slippers Womens Non Slip Indoor Home Bedroom Pool Spa Guest College Dorm
1
men slim jeans
Urbano Fashion Mens Slim Fit Jeans
1
Looking for a red cotton shirt
Cotton Regular Fit Solid Red Shirt
1
- Loss:
CoSENTLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "pairwise_cos_sim" }
Evaluation Dataset
Unnamed Dataset
- Size: 5 evaluation samples
- Columns:
sentence1
,sentence2
, andscore
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score type string string int details - min: 4 tokens
- mean: 5.6 tokens
- max: 8 tokens
- min: 8 tokens
- mean: 17.8 tokens
- max: 27 tokens
- 0: ~40.00%
- 1: ~60.00%
- Samples:
sentence1 sentence2 score sandal
Beslip Womens Mens Garden Clogs Shoes with Arch Support Unisex Comfort Slip-on Sandals
1
Looking for a men black jeans
DENNIE FOSTE Men Regular Mid Rise Black Jeans
1
comfortable running shoes
NYKD Everyday Stylish Running Sports Jacket with Pockets for Women
0
- Loss:
CoSENTLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "pairwise_cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_eval_batch_size
: 16learning_rate
: 3e-05warmup_ratio
: 0.1load_best_model_at_end
: Trueddp_find_unused_parameters
: False
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 8per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 3e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 3max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_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
: 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
: Trueignore_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
: Falseddp_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
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss | validation_spearman_max |
---|---|---|---|---|
0.3333 | 1 | 1.8178 | - | - |
0.6667 | 2 | 0.0 | - | - |
1.0 | 3 | 0.0 | 6.6023 | 0.2887 |
1.3333 | 4 | 0.0134 | - | - |
1.6667 | 5 | 0.0 | - | - |
2.0 | 6 | 0.0 | 6.5861 | 0.2887 |
Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.42.2
- PyTorch: 2.3.0
- Accelerate: 0.31.0
- 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",
}
CoSENTLoss
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}