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--- |
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license: mit |
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base_model: microsoft/deberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- squad_v2 |
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model-index: |
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- name: deberta-base-finetuned-squad2 |
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results: [] |
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language: |
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- en |
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metrics: |
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- exact_match |
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- f1 |
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pipeline_tag: question-answering |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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## Model description |
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DeBERTabase fine-tuned on SQuAD 2.0 : Encoder-based Transformer Language model. |
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DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. |
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It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.<br> |
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Suitable for Question-Answering tasks, predicts answer spans within the context provided.<br> |
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**Language model:** microsoft/deberta-base |
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**Language:** English |
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**Downstream-task:** Question-Answering |
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**Training data:** Train-set SQuAD 2.0 |
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**Evaluation data:** Evaluation-set SQuAD 2.0 |
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**Hardware Accelerator used**: GPU Tesla T4 |
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## Intended uses & limitations |
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For Question-Answering - |
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```python |
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!pip install transformers |
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from transformers import pipeline |
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model_checkpoint = "IProject-10/deberta-base-finetuned-squad2" |
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question_answerer = pipeline("question-answering", model=model_checkpoint) |
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context = """ |
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🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration |
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between them. It's straightforward to train your models with one before loading them for inference with the other. |
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""" |
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question = "Which deep learning libraries back 🤗 Transformers?" |
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question_answerer(question=question, context=context) |
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``` |
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## Results |
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Evaluation on SQuAD 2.0 validation dataset: |
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``` |
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exact: 81.03259496336226, |
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f1: 84.42279239924598, |
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total: 11873, |
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HasAns_exact: 79.30161943319838, |
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HasAns_f1: 86.09173653108105, |
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HasAns_total: 5928, |
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NoAns_exact: 82.75862068965517, |
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NoAns_f1: 82.75862068965517, |
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NoAns_total: 5945, |
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best_exact: 81.03259496336226, |
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best_exact_thresh: 0.9992604851722717, |
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best_f1: 84.42279239924635, |
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best_f1_thresh: 0.9992604851722717, |
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total_time_in_seconds: 326.41847440000004, |
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samples_per_second: 36.37355398411236, |
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latency_in_seconds: 0.027492501844521185 |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 0.8054 | 1.0 | 8238 | 0.7902 | |
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| 0.5368 | 2.0 | 16476 | 0.7901 | |
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| 0.3845 | 3.0 | 24714 | 0.9334 | |
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This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the squad_v2 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9334 |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.3 |
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- Tokenizers 0.13.3 |
