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deep-learning-analytics/triviaqa-t5-base deep-learning-analytics/triviaqa-t5-base
120 downloads
last 30 days

pytorch

tf

Contributed by

deep-learning-analytics Priya Dwivedi
2 models

How to use this model directly from the πŸ€—/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("deep-learning-analytics/triviaqa-t5-base") model = AutoModelForSeq2SeqLM.from_pretrained("deep-learning-analytics/triviaqa-t5-base")
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Model name

Closed Book Trivia-QA T5 base

Model description

This is a T5-base model trained on No Context Trivia QA data set. The input to the model is a Trivia type question. The model is tuned to search for the answer in its memory to return it. The pretrained model used here was trained on Common Crawl (C4) data set. The model was trained for 135 epochs using a batch size of 32 and learning rate of 1e-3. Max_input_lngth is set as 25 and max_output_length is 10. Model attained an EM score of 17 and a Subset Match score of 24.5 We have written a blog post that covers the training procedure. Please find it here.

Test the model on Trivia Questions from the websites below: https://www.triviaquestionss.com/easy-trivia-questions/ https://laffgaff.com/easy-trivia-questions-and-answers/

Usage

from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("deep-learning-analytics/triviaqa-t5-base")
model = AutoModelWithLMHead.from_pretrained("deep-learning-analytics/triviaqa-t5-base")

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)

text = "Who directed the movie Jaws?"

preprocess_text = text.strip().replace("\n","")
tokenized_text = tokenizer.encode(preprocess_text, return_tensors="pt").to(device)

outs = model.model.generate(
            tokenized_text,
            max_length=10,
            num_beams=2,
            early_stopping=True
           )

dec = [tokenizer.decode(ids) for ids in outs]
print("Predicted Answer: ", dec)