SentenceTransformer based on sentence-transformers/stsb-distilbert-base
This is a sentence-transformers model finetuned from sentence-transformers/stsb-distilbert-base on the sentence-transformers/quora-duplicates 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: sentence-transformers/stsb-distilbert-base
- Maximum Sequence Length: 128 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': 128, '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/stsb-distilbert-base-mnrl")
# Run inference
sentences = [
'Is Cicret a scam?',
'Is the Cicret Bracelet a scam?',
'Can you eat only once a day?',
]
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
Binary Classification
- Dataset:
quora-duplicates
- Evaluated with
BinaryClassificationEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.816 |
cosine_accuracy_threshold | 0.7867 |
cosine_f1 | 0.7286 |
cosine_f1_threshold | 0.7353 |
cosine_precision | 0.6746 |
cosine_recall | 0.7919 |
cosine_ap | 0.7731 |
dot_accuracy | 0.807 |
dot_accuracy_threshold | 150.9795 |
dot_f1 | 0.7224 |
dot_f1_threshold | 137.3444 |
dot_precision | 0.6641 |
dot_recall | 0.7919 |
dot_ap | 0.7492 |
manhattan_accuracy | 0.81 |
manhattan_accuracy_threshold | 195.8866 |
manhattan_f1 | 0.7246 |
manhattan_f1_threshold | 237.6859 |
manhattan_precision | 0.6293 |
manhattan_recall | 0.854 |
manhattan_ap | 0.7611 |
euclidean_accuracy | 0.81 |
euclidean_accuracy_threshold | 8.7739 |
euclidean_f1 | 0.7261 |
euclidean_f1_threshold | 10.8438 |
euclidean_precision | 0.6281 |
euclidean_recall | 0.8602 |
euclidean_ap | 0.7612 |
max_accuracy | 0.816 |
max_accuracy_threshold | 195.8866 |
max_f1 | 0.7286 |
max_f1_threshold | 237.6859 |
max_precision | 0.6746 |
max_recall | 0.8602 |
max_ap | 0.7731 |
Paraphrase Mining
- Dataset:
quora-duplicates-dev
- Evaluated with
ParaphraseMiningEvaluator
Metric | Value |
---|---|
average_precision | 0.5349 |
f1 | 0.5395 |
precision | 0.5175 |
recall | 0.5635 |
threshold | 0.762 |
Information Retrieval
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.9646 |
cosine_accuracy@3 | 0.9926 |
cosine_accuracy@5 | 0.9956 |
cosine_accuracy@10 | 0.9986 |
cosine_precision@1 | 0.9646 |
cosine_precision@3 | 0.4293 |
cosine_precision@5 | 0.2754 |
cosine_precision@10 | 0.1452 |
cosine_recall@1 | 0.8301 |
cosine_recall@3 | 0.9609 |
cosine_recall@5 | 0.9808 |
cosine_recall@10 | 0.9935 |
cosine_ndcg@10 | 0.9795 |
cosine_mrr@10 | 0.979 |
cosine_map@100 | 0.9718 |
dot_accuracy@1 | 0.9574 |
dot_accuracy@3 | 0.9876 |
dot_accuracy@5 | 0.9924 |
dot_accuracy@10 | 0.9978 |
dot_precision@1 | 0.9574 |
dot_precision@3 | 0.4257 |
dot_precision@5 | 0.2737 |
dot_precision@10 | 0.1447 |
dot_recall@1 | 0.8238 |
dot_recall@3 | 0.9538 |
dot_recall@5 | 0.9764 |
dot_recall@10 | 0.9918 |
dot_ndcg@10 | 0.9741 |
dot_mrr@10 | 0.9731 |
dot_map@100 | 0.9646 |
Training Details
Training Dataset
sentence-transformers/quora-duplicates
- Dataset: sentence-transformers/quora-duplicates at 451a485
- Size: 100,000 training samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 6 tokens
- mean: 13.85 tokens
- max: 42 tokens
- min: 6 tokens
- mean: 13.65 tokens
- max: 44 tokens
- min: 4 tokens
- mean: 14.76 tokens
- max: 64 tokens
- Samples:
anchor positive negative Why in India do we not have one on one political debate as in USA?
Why cant we have a public debate between politicians in India like the one in US?
Can people on Quora stop India Pakistan debate? We are sick and tired seeing this everyday in bulk?
What is OnePlus One?
How is oneplus one?
Why is OnePlus One so good?
Does our mind control our emotions?
How do smart and successful people control their emotions?
How can I control my positive emotions for the people whom I love but they don't care about me?
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Evaluation Dataset
sentence-transformers/quora-duplicates
- Dataset: sentence-transformers/quora-duplicates at 451a485
- Size: 1,000 evaluation samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 7 tokens
- mean: 13.84 tokens
- max: 43 tokens
- min: 6 tokens
- mean: 13.8 tokens
- max: 38 tokens
- min: 6 tokens
- mean: 14.71 tokens
- max: 56 tokens
- Samples:
anchor positive negative Which programming language is best for developing low-end games?
What coding language should I learn first for making games?
I am entering the world of video game programming and want to know what language I should learn? Because there are so many languages ​​I do not know which one to start with. Can you recommend a language that's easy to learn and can be used with many platforms?
Was it appropriate for Meryl Streep to use her Golden Globes speech to attack Donald Trump?
Should Meryl Streep be using her position to attack the president?
Why did Kelly Ann Conway say that Meryl Streep incited peoples worst feelings?
Where can I found excellent commercial fridges in Sydney?
Where can I found impressive range of commercial fridges in Sydney?
What is the best grocery delivery service in Sydney?
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 64per_device_eval_batch_size
: 64num_train_epochs
: 1warmup_ratio
: 0.1fp16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Falseper_device_train_batch_size
: 64per_device_eval_batch_size
: 64per_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
: 1max_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
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss | cosine_map@100 | quora-duplicates-dev_average_precision | quora-duplicates_max_ap |
---|---|---|---|---|---|---|
0 | 0 | - | - | 0.9245 | 0.4200 | 0.6890 |
0.0640 | 100 | 0.2535 | - | - | - | - |
0.1280 | 200 | 0.1732 | - | - | - | - |
0.1599 | 250 | - | 0.1021 | 0.9601 | 0.5033 | 0.7342 |
0.1919 | 300 | 0.1465 | - | - | - | - |
0.2559 | 400 | 0.1186 | - | - | - | - |
0.3199 | 500 | 0.1159 | 0.0773 | 0.9653 | 0.5247 | 0.7453 |
0.3839 | 600 | 0.1088 | - | - | - | - |
0.4479 | 700 | 0.0993 | - | - | - | - |
0.4798 | 750 | - | 0.0665 | 0.9666 | 0.5264 | 0.7655 |
0.5118 | 800 | 0.0952 | - | - | - | - |
0.5758 | 900 | 0.0799 | - | - | - | - |
0.6398 | 1000 | 0.0855 | 0.0570 | 0.9709 | 0.5391 | 0.7717 |
0.7038 | 1100 | 0.0804 | - | - | - | - |
0.7678 | 1200 | 0.073 | - | - | - | - |
0.7997 | 1250 | - | 0.0513 | 0.9719 | 0.5329 | 0.7662 |
0.8317 | 1300 | 0.0741 | - | - | - | - |
0.8957 | 1400 | 0.0699 | - | - | - | - |
0.9597 | 1500 | 0.0755 | 0.0476 | 0.9718 | 0.5349 | 0.7731 |
Environmental Impact
Carbon emissions were measured using CodeCarbon.
- Energy Consumed: 0.039 kWh
- Carbon Emitted: 0.015 kg of CO2
- Hours Used: 0.169 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",
}
MultipleNegativesRankingLoss
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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Model tree for tomaarsen/stsb-distilbert-base-mnrl
Base model
sentence-transformers/stsb-distilbert-baseEvaluation results
- Cosine Accuracy on quora duplicatesself-reported0.816
- Cosine Accuracy Threshold on quora duplicatesself-reported0.787
- Cosine F1 on quora duplicatesself-reported0.729
- Cosine F1 Threshold on quora duplicatesself-reported0.735
- Cosine Precision on quora duplicatesself-reported0.675
- Cosine Recall on quora duplicatesself-reported0.792
- Cosine Ap on quora duplicatesself-reported0.773
- Dot Accuracy on quora duplicatesself-reported0.807
- Dot Accuracy Threshold on quora duplicatesself-reported150.979
- Dot F1 on quora duplicatesself-reported0.722