SentenceTransformer based on distilbert/distilbert-base-uncased
This is a sentence-transformers model finetuned from distilbert/distilbert-base-uncased on the sentence-transformers/stsb dataset. It maps sentences & paragraphs to a 768-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: distilbert/distilbert-base-uncased
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
- Language: en
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': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, '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})
)
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("tomaarsen/distilbert-base-uncased-sts-2d-matryoshka")
# Run inference
sentences = [
'A parrot is talking.',
'A parrot is talking into a microphone.',
'A monkey pratices martial arts.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Semantic Similarity
- Dataset:
sts-dev
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.8619 |
spearman_cosine | 0.8713 |
pearson_manhattan | 0.8611 |
spearman_manhattan | 0.8619 |
pearson_euclidean | 0.8615 |
spearman_euclidean | 0.8624 |
pearson_dot | 0.7716 |
spearman_dot | 0.7816 |
pearson_max | 0.8619 |
spearman_max | 0.8713 |
Semantic Similarity
- Dataset:
sts-test
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.8282 |
spearman_cosine | 0.8373 |
pearson_manhattan | 0.8425 |
spearman_manhattan | 0.8374 |
pearson_euclidean | 0.8425 |
spearman_euclidean | 0.8373 |
pearson_dot | 0.675 |
spearman_dot | 0.6568 |
pearson_max | 0.8425 |
spearman_max | 0.8374 |
Training Details
Training Dataset
sentence-transformers/stsb
- Dataset: sentence-transformers/stsb at ab7a5ac
- Size: 5,749 training samples
- Columns:
sentence1
,sentence2
, andscore
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score type string string float details - min: 6 tokens
- mean: 10.0 tokens
- max: 28 tokens
- min: 5 tokens
- mean: 9.95 tokens
- max: 25 tokens
- min: 0.0
- mean: 0.54
- max: 1.0
- Samples:
sentence1 sentence2 score A plane is taking off.
An air plane is taking off.
1.0
A man is playing a large flute.
A man is playing a flute.
0.76
A man is spreading shreded cheese on a pizza.
A man is spreading shredded cheese on an uncooked pizza.
0.76
- Loss:
Matryoshka2dLoss
with these parameters:{ "loss": "CoSENTLoss", "n_layers_per_step": 1, "last_layer_weight": 1.0, "prior_layers_weight": 1.0, "kl_div_weight": 1.0, "kl_temperature": 0.3, "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": 1 }
Evaluation Dataset
sentence-transformers/stsb
- Dataset: sentence-transformers/stsb at ab7a5ac
- Size: 1,500 evaluation samples
- Columns:
sentence1
,sentence2
, andscore
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score type string string float details - min: 5 tokens
- mean: 15.1 tokens
- max: 45 tokens
- min: 6 tokens
- mean: 15.11 tokens
- max: 53 tokens
- min: 0.0
- mean: 0.47
- max: 1.0
- Samples:
sentence1 sentence2 score A man with a hard hat is dancing.
A man wearing a hard hat is dancing.
1.0
A young child is riding a horse.
A child is riding a horse.
0.95
A man is feeding a mouse to a snake.
The man is feeding a mouse to the snake.
1.0
- Loss:
Matryoshka2dLoss
with these parameters:{ "loss": "CoSENTLoss", "n_layers_per_step": 1, "last_layer_weight": 1.0, "prior_layers_weight": 1.0, "kl_div_weight": 1.0, "kl_temperature": 0.3, "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": 1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 16per_device_eval_batch_size
: 16num_train_epochs
: 4warmup_ratio
: 0.1fp16
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Falseper_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
: 1.0num_train_epochs
: 4max_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
: 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
: Nonedataloader_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
: proportional
Training Logs
Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
---|---|---|---|---|---|
0.2778 | 100 | 7.1781 | 6.6704 | 0.8345 | - |
0.5556 | 200 | 6.5316 | 6.7135 | 0.8439 | - |
0.8333 | 300 | 6.6267 | 6.8697 | 0.8551 | - |
1.1111 | 400 | 6.5709 | 6.7623 | 0.8568 | - |
1.3889 | 500 | 6.2898 | 6.4412 | 0.8644 | - |
1.6667 | 600 | 6.2021 | 6.7711 | 0.8595 | - |
1.9444 | 700 | 6.201 | 6.5252 | 0.8628 | - |
2.2222 | 800 | 6.0862 | 6.9795 | 0.8652 | - |
2.5 | 900 | 6.303 | 6.7339 | 0.8685 | - |
2.7778 | 1000 | 5.9031 | 6.7249 | 0.8694 | - |
3.0556 | 1100 | 6.0803 | 6.8350 | 0.8684 | - |
3.3333 | 1200 | 6.0564 | 6.9703 | 0.8695 | - |
3.6111 | 1300 | 5.8407 | 7.3822 | 0.8707 | - |
3.8889 | 1400 | 5.8229 | 7.0442 | 0.8713 | - |
4.0 | 1440 | - | - | - | 0.8373 |
Environmental Impact
Carbon emissions were measured using CodeCarbon.
- Energy Consumed: 0.014 kWh
- Carbon Emitted: 0.005 kg of CO2
- Hours Used: 0.072 hours
Training Hardware
- On Cloud: No
- GPU Model: 1 x NVIDIA GeForce RTX 3090
- CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
- RAM Size: 31.78 GB
Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.0.0.dev0
- Transformers: 4.41.0.dev0
- PyTorch: 2.3.0+cu121
- Accelerate: 0.26.1
- Datasets: 2.18.0
- 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",
}
Matryoshka2dLoss
@misc{li20242d,
title={2D Matryoshka Sentence Embeddings},
author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
year={2024},
eprint={2402.14776},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
MatryoshkaLoss
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
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},
}
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Model tree for tomaarsen/distilbert-base-uncased-sts-2d-matryoshka
Base model
distilbert/distilbert-base-uncasedEvaluation results
- Pearson Cosine on sts devself-reported0.862
- Spearman Cosine on sts devself-reported0.871
- Pearson Manhattan on sts devself-reported0.861
- Spearman Manhattan on sts devself-reported0.862
- Pearson Euclidean on sts devself-reported0.862
- Spearman Euclidean on sts devself-reported0.862
- Pearson Dot on sts devself-reported0.772
- Spearman Dot on sts devself-reported0.782
- Pearson Max on sts devself-reported0.862
- Spearman Max on sts devself-reported0.871