| 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() |
