---
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](https://www.SBERT.net) model finetuned from [sentence-transformers/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) on the [quora-duplicates](https://huggingface.co/datasets/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](https://huggingface.co/sentence-transformers/stsb-distilbert-base)
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
- **Language:** en
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator)
| Metric | Value |
|:----------------------|:-----------|
| **average_precision** | **0.5483** |
| f1 | 0.5606 |
| precision | 0.5539 |
| recall | 0.5675 |
| threshold | 0.8632 |
#### Information Retrieval
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* 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
| |
* 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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Evaluation Dataset
#### quora-duplicates
* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* 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
| |
* 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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#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