| from transformers import BartForSequenceClassification, BartTokenizer |
| import gradio as grad |
| bart_tkn = BartTokenizer.from_pretrained('facebook/bart-large-mnli') |
| mdl = BartForSequenceClassification.from_pretrained('facebook/bart-large-mnli') |
|
|
| def classify(text,label): |
| tkn_ids = bart_tkn.encode(text, label, return_tensors='pt') |
| tkn_lgts = mdl(tkn_ids)[0] |
| entail_contra_tkn_lgts = tkn_lgts[:,[0,2]] |
| probab = entail_contra_tkn_lgts.softmax(dim=1) |
| response = probab[:,1].item() * 100 |
| return response |
| txt=grad.Textbox(lines=1, label="English", placeholder="text to be classified") |
| labels=grad.Textbox(lines=1, label="Label", placeholder="Input a Label") |
| out=grad.Textbox(lines=1, label="Probablity of label being true is") |
| grad.Interface(classify, inputs=[txt,labels], outputs=out).launch() |
|
|
