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--- |
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language: |
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- en |
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license: cc-by-4.0 |
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tags: |
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- classification |
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datasets: |
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- SetFit/qqp |
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metrics: |
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- accuracy |
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- loss |
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thumbnail: https://github.com/AI-Ahmed |
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models: |
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- microsoft/deberta-v3-base |
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pipeline_tag: text-classification |
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widget: |
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- text: How is the life of a math student? Could you describe your own experiences? |
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Which level of preparation is enough for the exam jlpt5? |
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example_title: Difference Detection. |
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- text: What can one do after MBBS? What do i do after my MBBS? |
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example_title: Duplicates Detection. |
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model-index: |
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- name: deberta-v3-base-funetuned-cls-qqa |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: qqp |
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type: qqp |
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config: sst2 |
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split: validation |
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metrics: |
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- type: accuracy |
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value: 0.917969 |
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name: Accuracy |
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verified: true |
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verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzA2OWM4ZjJkYzZjNmM3YmNkODNhODYzOTMxY2RjZTZmODg4ODA4ZjJmNjFhNjkwZjFmZjk3YjBiNzhjNDAzOCIsInZlcnNpb24iOjF9.TqdmhhV_3fTWYHtM7SJj35ICZgZ6Ux7qYXwPHu8j0MkDmSeZfTniFCqB8LO8pqM1bK5iHQV1-vggZUdMu4spCA |
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- type: loss |
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value: 0.21741 |
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name: loss |
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verified: true |
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verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGQzZGZjNzZjNzFjOWViNjkyNGIxMGE5ZjA5ODAxOTNiZGQ5OTY4NWM1YThlZGEyZGRjOGE2MjkwYTRjN2Q2MyIsInZlcnNpb24iOjF9.ZxmqxdbOhAA8Ywz8_Q3aFaFG2kmTogFdWjlHgEa2JnGQWhL39VVtcn6A8gtekE_e3z5jsaNS4EnSzYVSWJZjAQ |
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--- |
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A fine-tuned model based on the **DeBERTaV3** model of Microsoft and fine-tuned on **Glue QQP**, which detects the linguistical similarities between two questions and whether they are duplicates questions or different. |
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## Model Hyperparameters |
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```python |
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epoch=4 |
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per_device_train_batch_size=32 |
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per_device_eval_batch_size=16 |
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lr=2e-5 |
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weight_decay=1e-2 |
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gradient_checkpointing=True |
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gradient_accumulation_steps=8 |
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``` |
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## Model Performance |
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|
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```JSON |
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{"Training Loss": 0.132400, |
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"Validation Loss": 0.217410, |
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"Validation Accuracy": 0.917969 |
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} |
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``` |
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## Model Dependencies |
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|
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```JSON |
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{"Main Model": "microsoft/deberta-v3-base", |
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"Dataset": "SetFit/qqp" |
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} |
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``` |
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## Training Monitoring & Performance |
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- [wandb - deberta_qqa_classification](https://wandb.ai/ai-ahmed/deberta_qqa_classification?workspace=user-ai-ahmed) |
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## Model Testing |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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model_name = "AI-Ahmed/deberta-v3-base-funetuned-cls-qqa" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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tokenized_input = tokenizer("How is the life of a math student? Could you describe your own experiences? Which level of preparation is enough for the exam jlpt5?", return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**tokenized_input).logits |
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predicted_class_id = logits.argmax().item() |
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model.config.id2label[predicted_class_id] |
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``` |
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## Information Citation |
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```bibtex |
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@inproceedings{ |
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he2021deberta, |
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title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION}, |
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author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen}, |
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booktitle={International Conference on Learning Representations}, |
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year={2021}, |
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url={https://openreview.net/forum?id=XPZIaotutsD} |
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} |
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``` |
