metadata
language: en
tags:
- generative qa
datasets:
- eli5
- stackexchange(pets, cooking, gardening, diy, crafts)
Work by Frederico Vicente & Diogo Tavares. 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.
Usage
from transformers import (
BartForConditionalGeneration,
BartTokenizer
)
import torch
import json
def read_json_file_2_dict(filename, store_dir='.'):
with open(f'{store_dir}/{filename}', 'r', encoding='utf-8') as file:
return json.load(file)
def get_device():
# If there's a GPU available...
if torch.cuda.is_available():
device = torch.device("cuda")
n_gpus = torch.cuda.device_count()
first_gpu = torch.cuda.get_device_name(0)
print(f'There are {n_gpus} GPU(s) available.')
print(f'GPU gonna be used: {first_gpu}')
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
return device
model_name = 'unlisboa/bart_qa_assistant'
tokenizer = BartTokenizer.from_pretrained(model_name)
device = get_device()
model = BartForConditionalGeneration.from_pretrained(model_name).to(device)
model.eval()
model_input = tokenizer(question, truncation=True, padding=True, return_tensors="pt")
generated_answers_encoded = model.generate(input_ids=model_input["input_ids"].to(device),attention_mask=model_input["attention_mask"].to(device),
force_words_ids=None,
min_length=1,
max_length=100,
do_sample=True,
early_stopping=True,
num_beams=4,
temperature=1.0,
top_k=None,
top_p=None,
# eos_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=2,
num_return_sequences=1,
return_dict_in_generate=True,
output_scores=True)
response = tokenizer.batch_decode(generated_answers_encoded['sequences'], skip_special_tokens=True,clean_up_tokenization_spaces=True)
print(response)
Have fun!