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from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
import torch | |
import gradio as gr | |
import numpy as np | |
MODEL_NAME = "cardiffnlp/twitter-xlm-roberta-base-sentiment" | |
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME) | |
labels = ['ααααα’αα£α α', 'αααα’α ααα£α α', 'ααααα’αα£α α'] | |
def classify_sentiment(text): | |
inputs = tokenizer(text, return_tensors="pt", truncation=True) | |
with torch.no_grad(): | |
outputs = model(**inputs) | |
logits = outputs.logits | |
probs = torch.nn.functional.softmax(logits, dim=1).numpy()[0] | |
# Get label and confidence | |
top_label = labels[np.argmax(probs)] | |
confidence = np.max(probs) | |
return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
iface = gr.Interface( | |
fn=classify_sentiment, | |
inputs=gr.Textbox(lines=3, placeholder="α¨ααα§ααααα α’ααα’α ..."), | |
outputs=gr.Label(num_top_classes=3), | |
title="Twitter-αα‘ αααα¬α§αααα‘ αααα‘αα€αααα’αα α", | |
description="αα§ααααα‘ CardiffNLP-αα‘ αα αααααααααα RoBERTa αααααα‘ α’ααα’αααα‘ ααααααα, αααα’α ααα£α αα α£αα α§αα€αααα αααα‘αα€ααͺαα αααα‘αααα‘." | |
) | |
iface.launch(share=True) |