metadata

language:
  - en
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:404290
  - loss:OnlineContrastiveLoss
base_model: sentence-transformers/stsb-distilbert-base
widget:
  - source_sentence: Why Modi is putting a ban on 500 and 1000 notes?
    sentences:
      - Why making multiple fake accounts on Quora is illegal?
      - >-
        What are the advantages of the decision taken by the Government of India
        to scrap out 500 and 1000 rupees notes?
      - Why should I go for internships?
  - source_sentence: Where can I buy cheap t-shirts?
    sentences:
      - Where can I buy cheap wholesale t-shirts?
      - How can I make money from a blog?
      - What are the best places to shop in Charleston, SC?
  - source_sentence: What are the most important mobile applications?
    sentences:
      - How can I tell if my wife's vagina had a bigger penis inside?
      - What is the most important apps in your phone?
      - >-
        What do you think Ned Stark would have done or said to Jon Snow if he
        was able to join the Night’s Watch or escaped his beheading?
  - source_sentence: What is the whole process for making Android games with high graphics?
    sentences:
      - What lf I don't accept Jesus as God?
      - >-
        I have to masturbate3 times to feel an orgasm sometimes only2 times what
        is wrong with me I went to the doctor and they do not believe meWhat's
        wrong?
      - What does a healthy diet consist of?
  - source_sentence: Why do so many religious people believe in healing miracles?
    sentences:
      - Is Warframe better than Destiny?
      - What do you like about China?
      - Is believing in God a bad thing?
datasets:
  - sentence-transformers/quora-duplicates
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - cosine_mcc
  - average_precision
  - f1
  - precision
  - recall
  - threshold
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: SentenceTransformer based on sentence-transformers/stsb-distilbert-base
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: quora duplicates
          type: quora-duplicates
        metrics:
          - type: cosine_accuracy
            value: 0.877
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.7857047319412231
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.8516284680337757
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.774639368057251
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.8209302325581396
            name: Cosine Precision
          - type: cosine_recall
            value: 0.8847117794486216
            name: Cosine Recall
          - type: cosine_ap
            value: 0.8988328505183655
            name: Cosine Ap
          - type: cosine_mcc
            value: 0.7483655051498526
            name: Cosine Mcc
      - task:
          type: paraphrase-mining
          name: Paraphrase Mining
        dataset:
          name: quora duplicates dev
          type: quora-duplicates-dev
        metrics:
          - type: average_precision
            value: 0.5483042026376685
            name: Average Precision
          - type: f1
            value: 0.5606415792720543
            name: F1
          - type: precision
            value: 0.5539301735907939
            name: Precision
          - type: recall
            value: 0.5675176100314733
            name: Recall
          - type: threshold
            value: 0.8631762564182281
            name: Threshold
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy@1
            value: 0.9308
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.969
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9778
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9854
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.9308
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.4145333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.26696000000000003
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.14144
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8008592901379665
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9314231047351341
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9558165998609235
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9743579383296442
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9511384841680516
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9511976190476192
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.939071878001028
            name: Cosine Map@100

SentenceTransformer based on sentence-transformers/stsb-distilbert-base

This is a sentence-transformers model finetuned from sentence-transformers/stsb-distilbert-base on the 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 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- quora-duplicates
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("omega5505/stsb-distilbert-base-ocl")
# Run inference
sentences = [
    'Why do so many religious people believe in healing miracles?',
    'Is believing in God a bad thing?',
    'What do you like about China?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Binary Classification

Dataset: quora-duplicates
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.877
cosine_accuracy_threshold	0.7857
cosine_f1	0.8516
cosine_f1_threshold	0.7746
cosine_precision	0.8209
cosine_recall	0.8847
cosine_ap	0.8988
cosine_mcc	0.7484

Paraphrase Mining

Dataset: quora-duplicates-dev
Evaluated with ParaphraseMiningEvaluator

Metric	Value
average_precision	0.5483
f1	0.5606
precision	0.5539
recall	0.5675
threshold	0.8632

Information Retrieval

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9308
cosine_accuracy@3	0.969
cosine_accuracy@5	0.9778
cosine_accuracy@10	0.9854
cosine_precision@1	0.9308
cosine_precision@3	0.4145
cosine_precision@5	0.267
cosine_precision@10	0.1414
cosine_recall@1	0.8009
cosine_recall@3	0.9314
cosine_recall@5	0.9558
cosine_recall@10	0.9744
cosine_ndcg@10	0.9511
cosine_mrr@10	0.9512
cosine_map@100	0.9391

Training Details

Training Dataset

quora-duplicates

Dataset: quora-duplicates at 451a485
Size: 404,290 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 6 tokens
mean: 15.73 tokens
max: 65 tokens

min: 6 tokens
mean: 15.93 tokens
max: 85 tokens

0: ~61.60%
1: ~38.40%

	sentence1	sentence2	label
type	string	string	int
details	min: 6 tokens mean: 15.73 tokens max: 65 tokens	min: 6 tokens mean: 15.93 tokens max: 85 tokens	0: ~61.60% 1: ~38.40%

Samples:

sentence1	sentence2	label
`How can Trump supporters claim he didn't mock a disabled reporter when there is live footage of him mocking a disabled reporter?`	`Why don't people actually watch the Trump video of him allegedly mocking a disabled reporter?`	`0`
`Where can I get the best digital marketing course (online & offline) in India?`	`Which is the best digital marketing institute for professionals in India?`	`1`
`What best two liner shayri?`	`What does "senile dementia, uncomplicated" mean in medical terms?`	`0`

Loss: OnlineContrastiveLoss

Evaluation Dataset

quora-duplicates

Dataset: quora-duplicates at 451a485
Size: 404,290 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 6 tokens
mean: 16.14 tokens
max: 70 tokens

min: 6 tokens
mean: 15.92 tokens
max: 74 tokens

0: ~60.10%
1: ~39.90%

	sentence1	sentence2	label
type	string	string	int
details	min: 6 tokens mean: 16.14 tokens max: 70 tokens	min: 6 tokens mean: 15.92 tokens max: 74 tokens	0: ~60.10% 1: ~39.90%

Samples:

sentence1	sentence2	label
`What are some must subscribe RSS feeds?`	`What are RSS feeds?`	`0`
`How close are Madonna and Hillary Clinton?`	`Why do people say Hillary Clinton is a crook?`	`0`
`Can you share best day of your life?`	`What is the Best Day of your life till date?`	`1`

Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: True
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
eval_use_gather_object: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	quora-duplicates_cosine_ap	quora-duplicates-dev_average_precision	cosine_ndcg@10
0	0	-	-	0.7458	0.4200	0.9390
0.0640	100	2.5263	-	-	-	-
0.1280	200	2.1489	-	-	-	-
0.1599	250	-	1.8621	0.8433	0.3907	0.9329
0.1919	300	2.0353	-	-	-	-
0.2559	400	1.7831	-	-	-	-
0.3199	500	1.8887	1.7744	0.8662	0.4924	0.9379
0.3839	600	1.7814	-	-	-	-
0.4479	700	1.7775	-	-	-	-
0.4798	750	-	1.6468	0.8766	0.4945	0.9399
0.5118	800	1.6835	-	-	-	-
0.5758	900	1.6974	-	-	-	-
0.6398	1000	1.5704	1.4925	0.8895	0.5283	0.9460
0.7038	1100	1.6771	-	-	-	-
0.7678	1200	1.619	-	-	-	-
0.7997	1250	-	1.4311	0.8982	0.5252	0.9466
0.8317	1300	1.6119	-	-	-	-
0.8957	1400	1.6043	-	-	-	-
0.9597	1500	1.6848	1.4070	0.8988	0.5483	0.9511

Framework Versions

Python: 3.9.18
Sentence Transformers: 3.4.1
Transformers: 4.44.2
PyTorch: 2.2.1+cu121
Accelerate: 1.3.0
Datasets: 2.19.0
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",
}