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--- |
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language: |
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- en |
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tags: |
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- gpt2 |
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license: mit |
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datasets: |
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- wiki_qa |
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inference: false |
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--- |
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## Description |
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This Question-Answering model was fine-tuned & trained from a generative, left-to-right transformer in the style of GPT-2, the [distilgpt2](https://huggingface.co/distilgpt2) model. This model was trained on [Wiki-QA](https://huggingface.co/datasets/wiki_qa) dataset from Microsoft. |
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# How to run XBOT-RK/Distil-GPT2-Wiki-QA using Transformers |
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## Question-Answering |
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The following code shows how to use the Distil-GPT2-Wiki-QA checkpoint and Transformers to generate Answers. |
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```python |
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from transformers import GPT2LMHeadModel, GPT2Tokenizer |
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import torch |
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import re |
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tokenizer = GPT2Tokenizer.from_pretrained("XBOT-RK/distilgpt2-wiki-qa") |
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model = GPT2LMHeadModel.from_pretrained("XBOT-RK/distilgpt2-wiki-qa") |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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def infer(question): |
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generated_tensor = model.generate(**tokenizer(question, return_tensors="pt").to(device), max_new_tokens = 50) |
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generated_text = tokenizer.decode(generated_tensor[0]) |
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return generated_text |
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def processAnswer(question, result): |
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answer = result.replace(question, '').strip() |
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if "<bot>:" in answer: |
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answer = re.search('<bot>:(.*)', answer).group(1).strip() |
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if "<endofstring>" in answer: |
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answer = re.search('(.*)<endofstring>', answer).group(1).strip() |
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return answer |
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question = "What is a tropical cyclone?" |
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result = infer(question) |
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answer = processAnswer(question, result) |
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print('Question: ', question) |
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print('Answer: ', answer) |
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# Output |
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"Question: What is a tropical cyclone?" |
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"Answer: The cyclone is named after the climber Edmond Halley, who described it as the 'most powerful cyclone of the Atlantic'." |
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``` |
