|
import transformers |
|
import gradio as gr |
|
import git |
|
import os |
|
os.system("pip install --upgrade pip") |
|
|
|
|
|
import git |
|
git.Git("arabert").clone("https://github.com/aub-mind/arabert") |
|
from arabert.preprocess import ArabertPreprocessor |
|
arabert_prep = ArabertPreprocessor(model_name="bert-base-arabert", keep_emojis=False) |
|
|
|
|
|
|
|
from transformers import EncoderDecoderModel, AutoTokenizer |
|
tokenizer = AutoTokenizer.from_pretrained("tareknaous/bert2bert-empathetic-response-msa") |
|
model = EncoderDecoderModel.from_pretrained("tareknaous/bert2bert-empathetic-response-msa") |
|
model.eval() |
|
|
|
def generate_response(text, minimum_length, p, temperature): |
|
text_clean = arabert_prep.preprocess(text) |
|
inputs = tokenizer.encode_plus(text_clean,return_tensors='pt') |
|
outputs = model.generate(input_ids = inputs.input_ids, |
|
attention_mask = inputs.attention_mask, |
|
do_sample = True, |
|
min_length=minimum_length, |
|
top_p = p, |
|
temperature = temperature) |
|
preds = tokenizer.batch_decode(outputs) |
|
response = str(preds) |
|
response = response.replace("\'", '') |
|
response = response.replace("[[CLS]", '') |
|
response = response.replace("[SEP]]", '') |
|
response = str(arabert_prep.desegment(response)) |
|
return response |
|
|
|
title = 'Empathetic Response Generation in Arabic' |
|
description = 'This demo is for a BERT2BERT model trained for single-turn open-domain empathetic dialogue response generation in Modern Standard Arabic' |
|
gr.Interface(fn=generate_response, |
|
inputs=[ |
|
gr.inputs.Textbox(), |
|
gr.inputs.Slider(5, 20, step=1, label='Minimum Output Length'), |
|
gr.inputs.Slider(0.7, 1, step=0.1, label='Top-P'), |
|
gr.inputs.Slider(1, 3, step=0.1, label='Temperature'), |
|
], |
|
outputs="text", |
|
title=title, |
|
description=description).launch() |
