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yahyaabd
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stsb-distilbert-base-ocl

Sentence Similarity
sentence-transformers
Safetensors
English
distilbert
feature-extraction
Generated from Trainer
dataset_size:404290
loss:OnlineContrastiveLoss
Eval Results
text-embeddings-inference
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 ---
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: What does the lock symbol on my iPhone 6 means?
sentences:
- How did the Soviet Navy compare to the US Navy?
- What does the iPhone icon with lock and arrow mean?
- What is the importance of electrical engineering?
- source_sentence: Why are blue and red neon lights illegal or restricted for commercial
uses in Honduras?
sentences:
- Why are blue and red neon lights illegal or restricted for commercial uses in
Colombia?
- Why would I want a Raspberry Pi?
- How do I see things as they are?
- source_sentence: How will Hillary Clinton deal with russia?
sentences:
- What would have happened if Barty crouch Jr escaped the dementors and made it
back to the graveyard?
- How will Hillary Clinton deal with terrorism?
- I am a commercial student who wishes to study accounting, but now I wish to study
law. Is it possible?
- source_sentence: What are the best managing skills?
sentences:
- What are the top skills of effective Product Managers?
- How do I lose weight in a short time?
- What are some good songs for lyrical dances?
- source_sentence: What is the best fact checking sources that all Quorans will most
trust?
sentences:
- Do people still write love letters?
- Is working in McKinsey one of the best and surest ways to get into Harvard Business
School?
- What is the most memorable book that Quorans have read?
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: 0.869
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.813665509223938
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.8390243902439025
name: Cosine F1
- type: cosine_f1_threshold
value: 0.7617226243019104
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.7818181818181819
name: Cosine Precision
- type: cosine_recall
value: 0.9052631578947369
name: Cosine Recall
- type: cosine_ap
value: 0.8852756469769394
name: Cosine Ap
- type: cosine_mcc
value: 0.7337941850587686
name: Cosine Mcc
- task:
type: paraphrase-mining
name: Paraphrase Mining
dataset:
name: quora duplicates dev
type: quora-duplicates-dev
metrics:
- type: average_precision
value: 0.5427423938771084
name: Average Precision
- type: f1
value: 0.5532539228607665
name: F1
- type: precision
value: 0.5508021390374331
name: Precision
- type: recall
value: 0.5557276315132138
name: Recall
- type: threshold
value: 0.865865558385849
name: Threshold
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.9298
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9732
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.982
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9868
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9298
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4154
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.26792
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.1417
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8009069531416296
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9349178789609083
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9610774822138647
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9765400300287947
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9525570390902354
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9522342063492065
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9400294978560327
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) <!-- at revision a560fa5fec90547a51a4a41a392d4aef93b49f16 -->
- **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
<!-- - **License:** Unknown -->
### 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("yahyaabd/stsb-distilbert-base-ocl")
# Run inference
sentences = [
'What is the best fact checking sources that all Quorans will most trust?',
'What is the most memorable book that Quorans have read?',
'Is working in McKinsey one of the best and surest ways to get into Harvard Business School?',
]
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]
```
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## Evaluation
### Metrics
#### Binary Classification
* Dataset: `quora-duplicates`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:--------------------------|:-----------|
| cosine_accuracy | 0.869 |
| cosine_accuracy_threshold | 0.8137 |
| cosine_f1 | 0.839 |
| cosine_f1_threshold | 0.7617 |
| cosine_precision | 0.7818 |
| cosine_recall | 0.9053 |
| **cosine_ap** | **0.8853** |
| cosine_mcc | 0.7338 |
#### Paraphrase Mining
* Dataset: `quora-duplicates-dev`
* Evaluated with [<code>ParaphraseMiningEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator)
| Metric | Value |
|:----------------------|:-----------|
| **average_precision** | **0.5427** |
| f1 | 0.5533 |
| precision | 0.5508 |
| recall | 0.5557 |
| threshold | 0.8659 |
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9298 |
| cosine_accuracy@3 | 0.9732 |
| cosine_accuracy@5 | 0.982 |
| cosine_accuracy@10 | 0.9868 |
| cosine_precision@1 | 0.9298 |
| cosine_precision@3 | 0.4154 |
| cosine_precision@5 | 0.2679 |
| cosine_precision@10 | 0.1417 |
| cosine_recall@1 | 0.8009 |
| cosine_recall@3 | 0.9349 |
| cosine_recall@5 | 0.9611 |
| cosine_recall@10 | 0.9765 |
| **cosine_ndcg@10** | **0.9526** |
| cosine_mrr@10 | 0.9522 |
| cosine_map@100 | 0.94 |
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## 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: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 16.01 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.9 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~64.40%</li><li>1: ~35.60%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:----------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>How much worse do things need to get before the "blue" states cut off welfare to the "red" states?</code> | <code>If the red states and the blue states were separated into two countries, which country would be more successful?</code> | <code>0</code> |
| <code>Can you offer me any advice on how to lose weight?</code> | <code>What are the best ways to lose weight? What is the best diet plan?</code> | <code>1</code> |
| <code>How do I break my knee?</code> | <code>How do I break my elbow?</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](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: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.98 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.9 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>0: ~62.00%</li><li>1: ~38.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:---------------|
| <code>Which is the best SAP online training centre at Hyderabad?</code> | <code>Which is the best sap workflow online training institute in Hyderabad?</code> | <code>1</code> |
| <code>How did World War Two start?</code> | <code>What will most likely cause World War III?</code> | <code>0</code> |
| <code>How do I find a unique string from a given string in Java without methods such as split, contain, and divide?</code> | <code>How can I split the string "[] {() <>} []" into " [,], {, (, ..." in Java?</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](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
<details><summary>Click to expand</summary>
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | quora-duplicates_cosine_ap | quora-duplicates-dev_average_precision | cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:--------------------------:|:--------------------------------------:|:--------------:|
| 0 | 0 | - | - | 0.7402 | 0.4200 | 0.9413 |
| 0.0640 | 100 | 2.481 | - | - | - | - |
| 0.1280 | 200 | 2.1466 | - | - | - | - |
| 0.1599 | 250 | - | 1.7997 | 0.8327 | 0.4596 | 0.9355 |
| 0.1919 | 300 | 2.0354 | - | - | - | - |
| 0.2559 | 400 | 1.9342 | - | - | - | - |
| 0.3199 | 500 | 1.9132 | 1.6231 | 0.8617 | 0.4896 | 0.9425 |
| 0.3839 | 600 | 1.8015 | - | - | - | - |
| 0.4479 | 700 | 1.7407 | - | - | - | - |
| 0.4798 | 750 | - | 1.4953 | 0.8737 | 0.5112 | 0.9468 |
| 0.5118 | 800 | 1.6454 | - | - | - | - |
| 0.5758 | 900 | 1.6568 | - | - | - | - |
| 0.6398 | 1000 | 1.6811 | 1.4678 | 0.8751 | 0.5290 | 0.9457 |
| 0.7038 | 1100 | 1.711 | - | - | - | - |
| 0.7678 | 1200 | 1.6449 | - | - | - | - |
| 0.7997 | 1250 | - | 1.4363 | 0.8811 | 0.5327 | 0.9507 |
| 0.8317 | 1300 | 1.5921 | - | - | - | - |
| 0.8957 | 1400 | 1.5062 | - | - | - | - |
| 0.9597 | 1500 | 1.5728 | 1.4029 | 0.8853 | 0.5427 | 0.9526 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.4.0
- Transformers: 4.48.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@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",
}
```
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