bart-large-mnli-yahoo-answers EU AI Act Compliance Profile

bart-lage-mnli-yahoo-answers

Model Description

This model takes facebook/bart-large-mnli and fine-tunes it on Yahoo Answers topic classification. It can be used to predict whether a topic label can be assigned to a given sequence, whether or not the label has been seen before.

You can play with an interactive demo of this zero-shot technique with this model, as well as the non-finetuned facebook/bart-large-mnli, here.

Intended Usage

This model was fine-tuned on topic classification and will perform best at zero-shot topic classification. Use hypothesis_template="This text is about {}." as this is the template used during fine-tuning.

For settings other than topic classification, you can use any model pre-trained on MNLI such as facebook/bart-large-mnli or roberta-large-mnli with the same code as written below.

With the zero-shot classification pipeline

The model can be used with the zero-shot-classification pipeline like so:

from transformers import pipeline
nlp = pipeline("zero-shot-classification", model="joeddav/bart-large-mnli-yahoo-answers")

sequence_to_classify = "Who are you voting for in 2020?"
candidate_labels = ["Europe", "public health", "politics", "elections"]
hypothesis_template = "This text is about {}."
nlp(sequence_to_classify, candidate_labels, multi_class=True, hypothesis_template=hypothesis_template)

With manual PyTorch

# pose sequence as a NLI premise and label as a hypothesis
from transformers import BartForSequenceClassification, BartTokenizer
nli_model = BartForSequenceClassification.from_pretrained('joeddav/bart-large-mnli-yahoo-answers')
tokenizer = BartTokenizer.from_pretrained('joeddav/bart-large-mnli-yahoo-answers')

premise = sequence
hypothesis = f

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