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--- |
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language: en |
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tags: |
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- generative qa |
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datasets: |
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- eli5 |
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- stackexchange(pets, cooking, gardening, diy, crafts) |
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--- |
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Work by [Frederico Vicente](https://huggingface.co/mrvicente) & [Diogo Tavares](https://huggingface.co/d-c-t). We finetuned BART Large for the task of generative question answering. It was trained on eli5, askScience and stackexchange using the following forums: pets, cooking, gardening, diy, crafts. |
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Check demo: https://huggingface.co/spaces/unlisboa/bart_qa_assistant |
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### Usage |
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```python |
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from transformers import ( |
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BartForConditionalGeneration, |
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BartTokenizer |
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) |
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import torch |
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import json |
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def read_json_file_2_dict(filename, store_dir='.'): |
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with open(f'{store_dir}/{filename}', 'r', encoding='utf-8') as file: |
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return json.load(file) |
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def get_device(): |
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# If there's a GPU available... |
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if torch.cuda.is_available(): |
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device = torch.device("cuda") |
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n_gpus = torch.cuda.device_count() |
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first_gpu = torch.cuda.get_device_name(0) |
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print(f'There are {n_gpus} GPU(s) available.') |
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print(f'GPU gonna be used: {first_gpu}') |
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else: |
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print('No GPU available, using the CPU instead.') |
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device = torch.device("cpu") |
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return device |
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model_name = 'unlisboa/bart_qa_assistant' |
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tokenizer = BartTokenizer.from_pretrained(model_name) |
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device = get_device() |
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model = BartForConditionalGeneration.from_pretrained(model_name).to(device) |
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model.eval() |
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model_input = tokenizer(question, truncation=True, padding=True, return_tensors="pt") |
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generated_answers_encoded = model.generate(input_ids=model_input["input_ids"].to(device),attention_mask=model_input["attention_mask"].to(device), |
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force_words_ids=None, |
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min_length=1, |
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max_length=100, |
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do_sample=True, |
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early_stopping=True, |
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num_beams=4, |
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temperature=1.0, |
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top_k=None, |
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top_p=None, |
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# eos_token_id=tokenizer.eos_token_id, |
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no_repeat_ngram_size=2, |
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num_return_sequences=1, |
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return_dict_in_generate=True, |
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output_scores=True) |
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response = tokenizer.batch_decode(generated_answers_encoded['sequences'], skip_special_tokens=True,clean_up_tokenization_spaces=True) |
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print(response) |
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``` |
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Have fun! |
