| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import gradio as grad | |
| tokenizer = AutoTokenizer.from_pretrained("oigele/Fb_improved_zeroshot") | |
| model = AutoModelForSequenceClassification.from_pretrained("oigele/Fb_improved_zeroshot") | |
| 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() |