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--- |
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language: |
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- en |
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tags: |
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- sentence-transformers |
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- cross-encoder |
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- text-classification |
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- generated_from_trainer |
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- dataset_size:404290 |
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- loss:BinaryCrossEntropyLoss |
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base_model: distilbert/distilroberta-base |
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datasets: |
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- sentence-transformers/quora-duplicates |
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pipeline_tag: text-classification |
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library_name: sentence-transformers |
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metrics: |
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- accuracy |
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- accuracy_threshold |
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- f1 |
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- f1_threshold |
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- precision |
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- recall |
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- average_precision |
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co2_eq_emissions: |
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emissions: 26.889480385249758 |
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energy_consumed: 0.06917762292257246 |
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source: codecarbon |
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training_type: fine-tuning |
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on_cloud: false |
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cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K |
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ram_total_size: 31.777088165283203 |
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hours_used: 0.214 |
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hardware_used: 1 x NVIDIA GeForce RTX 3090 |
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model-index: |
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- name: CrossEncoder based on distilbert/distilroberta-base |
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results: |
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- task: |
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type: cross-encoder-classification |
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name: Cross Encoder Classification |
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dataset: |
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name: quora duplicates dev |
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type: quora-duplicates-dev |
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metrics: |
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- type: accuracy |
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value: 0.8938 |
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name: Accuracy |
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- type: accuracy_threshold |
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value: 0.5088549852371216 |
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name: Accuracy Threshold |
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- type: f1 |
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value: 0.8612281373675477 |
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name: F1 |
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- type: f1_threshold |
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value: 0.3856155276298523 |
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name: F1 Threshold |
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- type: precision |
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value: 0.8182920912178554 |
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name: Precision |
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- type: recall |
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value: 0.908919428725411 |
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name: Recall |
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- type: average_precision |
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value: 0.920292628179356 |
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name: Average Precision |
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- task: |
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type: cross-encoder-classification |
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name: Cross Encoder Classification |
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dataset: |
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name: quora duplicates test |
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type: quora-duplicates-test |
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metrics: |
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- type: accuracy |
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value: 0.8938 |
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name: Accuracy |
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- type: accuracy_threshold |
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value: 0.5091445446014404 |
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name: Accuracy Threshold |
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- type: f1 |
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value: 0.8612281373675477 |
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name: F1 |
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- type: f1_threshold |
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value: 0.38580775260925293 |
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name: F1 Threshold |
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- type: precision |
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value: 0.8182920912178554 |
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name: Precision |
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- type: recall |
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value: 0.908919428725411 |
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name: Recall |
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- type: average_precision |
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value: 0.92029239602284 |
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name: Average Precision |
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--- |
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# CrossEncoder based on distilbert/distilroberta-base |
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This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Cross Encoder |
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- **Base model:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b --> |
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- **Maximum Sequence Length:** 514 tokens |
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- **Training Dataset:** |
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- [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) |
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- **Language:** en |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder) |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import CrossEncoder |
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# Download from the 🤗 Hub |
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model = CrossEncoder("sentence_transformers_model_id") |
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# Get scores for pairs... |
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pairs = [ |
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['What is the step by step guide to invest in share market in india?', 'What is the step by step guide to invest in share market?'], |
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['What is the story of Kohinoor (Koh-i-Noor) Diamond?', 'What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?'], |
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['How can I increase the speed of my internet connection while using a VPN?', 'How can Internet speed be increased by hacking through DNS?'], |
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['Why am I mentally very lonely? How can I solve it?', 'Find the remainder when [math]23^{24}[/math] is divided by 24,23?'], |
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['Which one dissolve in water quikly sugar, salt, methane and carbon di oxide?', 'Which fish would survive in salt water?'], |
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] |
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scores = model.predict(pairs) |
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print(scores.shape) |
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# [5] |
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# ... or rank different texts based on similarity to a single text |
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ranks = model.rank( |
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'What is the step by step guide to invest in share market in india?', |
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[ |
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'What is the step by step guide to invest in share market?', |
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'What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?', |
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'How can Internet speed be increased by hacking through DNS?', |
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'Find the remainder when [math]23^{24}[/math] is divided by 24,23?', |
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'Which fish would survive in salt water?', |
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] |
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) |
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# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Cross Encoder Classification |
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* Datasets: `quora-duplicates-dev` and `quora-duplicates-test` |
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* Evaluated with [<code>CEClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CEClassificationEvaluator) |
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| Metric | quora-duplicates-dev | quora-duplicates-test | |
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|:----------------------|:---------------------|:----------------------| |
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| accuracy | 0.8938 | 0.8938 | |
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| accuracy_threshold | 0.5089 | 0.5091 | |
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| f1 | 0.8612 | 0.8612 | |
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| f1_threshold | 0.3856 | 0.3858 | |
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| precision | 0.8183 | 0.8183 | |
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| recall | 0.9089 | 0.9089 | |
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| **average_precision** | **0.9203** | **0.9203** | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### quora-duplicates |
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* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) |
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* Size: 404,290 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | label | |
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|:--------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 1 characters</li><li>mean: 59.15 characters</li><li>max: 354 characters</li></ul> | <ul><li>min: 6 characters</li><li>mean: 60.74 characters</li><li>max: 399 characters</li></ul> | <ul><li>0: ~64.20%</li><li>1: ~35.80%</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | label | |
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|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <code>What are the features of the Indian caste system?</code> | <code>What triggers you the most when you play video games?</code> | <code>0</code> | |
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| <code>What is the best place to learn Mandarin Chinese in Singapore?</code> | <code>What is the best place in Singapore for durian in December?</code> | <code>0</code> | |
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| <code>What will be Hillary Clinton's India policy if she wins the election?</code> | <code>How would the bilateral relationship between India and the USA be under Hillary Clinton's presidency?</code> | <code>1</code> | |
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* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#binarycrossentropyloss) |
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### Evaluation Dataset |
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#### quora-duplicates |
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* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) |
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* Size: 404,290 evaluation samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | label | |
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|:--------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 11 characters</li><li>mean: 57.9 characters</li><li>max: 244 characters</li></ul> | <ul><li>min: 12 characters</li><li>mean: 59.33 characters</li><li>max: 221 characters</li></ul> | <ul><li>0: ~62.00%</li><li>1: ~38.00%</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | label | |
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|:---------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:---------------| |
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| <code>What is the step by step guide to invest in share market in india?</code> | <code>What is the step by step guide to invest in share market?</code> | <code>0</code> | |
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| <code>What is the story of Kohinoor (Koh-i-Noor) Diamond?</code> | <code>What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?</code> | <code>0</code> | |
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| <code>How can I increase the speed of my internet connection while using a VPN?</code> | <code>How can Internet speed be increased by hacking through DNS?</code> | <code>0</code> | |
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* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#binarycrossentropyloss) |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `num_train_epochs`: 1 |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | Validation Loss | quora-duplicates-dev_average_precision | quora-duplicates-test_average_precision | |
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|:------:|:----:|:-------------:|:---------------:|:--------------------------------------:|:---------------------------------------:| |
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| -1 | -1 | - | - | 0.3711 | - | |
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| 0.0167 | 100 | 0.6574 | - | - | - | |
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| 0.0333 | 200 | 0.4804 | - | - | - | |
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| 0.0500 | 300 | 0.4406 | - | - | - | |
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| 0.0666 | 400 | 0.4208 | - | - | - | |
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| 0.0833 | 500 | 0.3929 | 0.3958 | 0.8210 | - | |
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| 0.0999 | 600 | 0.3986 | - | - | - | |
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| 0.1166 | 700 | 0.3743 | - | - | - | |
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| 0.1332 | 800 | 0.3938 | - | - | - | |
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| 0.1499 | 900 | 0.3602 | - | - | - | |
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| 0.1665 | 1000 | 0.3714 | 0.3437 | 0.8565 | - | |
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| 0.1832 | 1100 | 0.3486 | - | - | - | |
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| 0.1998 | 1200 | 0.3479 | - | - | - | |
|
| 0.2165 | 1300 | 0.3417 | - | - | - | |
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| 0.2331 | 1400 | 0.3425 | - | - | - | |
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| 0.2498 | 1500 | 0.3353 | 0.3264 | 0.8742 | - | |
|
| 0.2664 | 1600 | 0.3335 | - | - | - | |
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| 0.2831 | 1700 | 0.3274 | - | - | - | |
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| 0.2998 | 1800 | 0.3284 | - | - | - | |
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| 0.3164 | 1900 | 0.3118 | - | - | - | |
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| 0.3331 | 2000 | 0.3073 | 0.3282 | 0.8826 | - | |
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| 0.3497 | 2100 | 0.3233 | - | - | - | |
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| 0.3664 | 2200 | 0.3072 | - | - | - | |
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| 0.3830 | 2300 | 0.314 | - | - | - | |
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| 0.3997 | 2400 | 0.3065 | - | - | - | |
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| 0.4163 | 2500 | 0.3046 | 0.2877 | 0.8930 | - | |
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| 0.4330 | 2600 | 0.2857 | - | - | - | |
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| 0.4496 | 2700 | 0.285 | - | - | - | |
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| 0.4663 | 2800 | 0.2957 | - | - | - | |
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| 0.4829 | 2900 | 0.2965 | - | - | - | |
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| 0.4996 | 3000 | 0.2824 | 0.2842 | 0.8998 | - | |
|
| 0.5162 | 3100 | 0.3019 | - | - | - | |
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| 0.5329 | 3200 | 0.2841 | - | - | - | |
|
| 0.5495 | 3300 | 0.2981 | - | - | - | |
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| 0.5662 | 3400 | 0.2878 | - | - | - | |
|
| 0.5828 | 3500 | 0.278 | 0.2803 | 0.9061 | - | |
|
| 0.5995 | 3600 | 0.2841 | - | - | - | |
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| 0.6162 | 3700 | 0.2794 | - | - | - | |
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| 0.6328 | 3800 | 0.2808 | - | - | - | |
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| 0.6495 | 3900 | 0.27 | - | - | - | |
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| 0.6661 | 4000 | 0.2719 | 0.2697 | 0.9091 | - | |
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| 0.6828 | 4100 | 0.2792 | - | - | - | |
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| 0.6994 | 4200 | 0.2669 | - | - | - | |
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| 0.7161 | 4300 | 0.2696 | - | - | - | |
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| 0.7327 | 4400 | 0.2642 | - | - | - | |
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| 0.7494 | 4500 | 0.2684 | 0.2591 | 0.9140 | - | |
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| 0.7660 | 4600 | 0.2593 | - | - | - | |
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| 0.7827 | 4700 | 0.2756 | - | - | - | |
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| 0.7993 | 4800 | 0.2584 | - | - | - | |
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| 0.8160 | 4900 | 0.2525 | - | - | - | |
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| 0.8326 | 5000 | 0.267 | 0.2540 | 0.9168 | - | |
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| 0.8493 | 5100 | 0.2612 | - | - | - | |
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| 0.8659 | 5200 | 0.2607 | - | - | - | |
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| 0.8826 | 5300 | 0.2565 | - | - | - | |
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| 0.8993 | 5400 | 0.2432 | - | - | - | |
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| 0.9159 | 5500 | 0.2568 | 0.2489 | 0.9198 | - | |
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| 0.9326 | 5600 | 0.2572 | - | - | - | |
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| 0.9492 | 5700 | 0.2658 | - | - | - | |
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| 0.9659 | 5800 | 0.2568 | - | - | - | |
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| 0.9825 | 5900 | 0.2539 | - | - | - | |
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| 0.9992 | 6000 | 0.2458 | 0.2503 | 0.9203 | - | |
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| -1 | -1 | - | - | - | 0.9203 | |
|
|
|
|
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### Environmental Impact |
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Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
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- **Energy Consumed**: 0.069 kWh |
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- **Carbon Emitted**: 0.027 kg of CO2 |
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- **Hours Used**: 0.214 hours |
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|
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### Training Hardware |
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- **On Cloud**: No |
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- **GPU Model**: 1 x NVIDIA GeForce RTX 3090 |
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- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K |
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- **RAM Size**: 31.78 GB |
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|
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### Framework Versions |
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- Python: 3.11.6 |
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- Sentence Transformers: 3.5.0.dev0 |
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- Transformers: 4.49.0.dev0 |
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- PyTorch: 2.5.0+cu121 |
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- Accelerate: 1.3.0 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.21.0 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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