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| import numpy as np | |
| import os | |
| import gradio as gr | |
| os.environ["WANDB_DISABLED"] = "true" | |
| from datasets import load_dataset, load_metric | |
| from transformers import ( | |
| AutoConfig, | |
| # AutoModelForSequenceClassification, | |
| AutoTokenizer, | |
| TrainingArguments, | |
| logging, | |
| pipeline | |
| ) | |
| # model_name = | |
| # tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| # config = AutoConfig.from_pretrained(model_name) | |
| # pipe = pipeline("text-classification") | |
| # pipe("This restaurant is awesome") | |
| label2id = { | |
| "LABEL_0": "negative", | |
| "LABEL_1": "neutral", | |
| "LABEL_2": "positive" | |
| } | |
| analyzer = pipeline( | |
| "sentiment-analysis", model="thak123/Cro-Frida", tokenizer="EMBEDDIA/crosloengual-bert" | |
| ) | |
| def predict_sentiment(x): | |
| return label2id[analyzer(x)[0]["label"]] | |
| interface = gr.Interface( | |
| fn=predict_sentiment, | |
| inputs='text', | |
| outputs=['text'], | |
| title='Croatian Movie reviews Sentiment Analysis', | |
| examples= ["Volim kavu","Ne volim kavu"], | |
| description='Get the positive/neutral/negative sentiment for the given input.' | |
| ) | |
| interface.launch(inline = False) | |