| import gradio as gr |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch.nn as nn |
|
|
| name = ["negative","neutral","positive"] |
|
|
|
|
| def main_note(sentence,aspect): |
| tokenizer = AutoTokenizer.from_pretrained("yangheng/deberta-v3-base-absa-v1.1") |
| model = AutoModelForSequenceClassification.from_pretrained("yangheng/deberta-v3-base-absa-v1.1") |
| |
| input_str = "[CLS]" + sentence + "[SEP]" + aspect + "[SEP]" |
| |
| inputs = tokenizer(input_str, return_tensors="pt") |
| outputs = model(**inputs) |
| softmax = nn.Softmax(dim=1) |
| outputs = softmax(outputs.logits) |
| result = [round(i,4) for i in outputs.tolist()[0]] |
| |
| return dict(zip(name,result)) |
|
|
| |
|
|
| iface = gr.Interface( |
| fn = main_note, |
| inputs=["text","text"], |
| outputs = gr.outputs.Label(), |
| examples=[["1.) Instead of being at the back of the oven, the cord is attached at the front right side.","cord"], |
| ["The pan I received was not in the same league as my old pan, new is cheap feeling and does not have a plate on the bottom.","pan"], |
| ["The pan I received was not in the same league as my old pan, new is cheap feeling and does not have a plate on the bottom.","bottom"], |
| ["They seem much more durable and less prone to staining, retaining their white properties for a much longer period of time.","durability"], |
| ["It took some time to clean and maintain, but totally worth it!","clean"], |
| ["this means that not only will the smallest burner heat up the pan, but it will also vertically heat up 1\" of the handle.","handle"]]) |
|
|
| iface.launch() |
|
|
