| | |
| | from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline |
| | from transformers_interpret import SequenceClassificationExplainer |
| | model = AutoModelForSequenceClassification.from_pretrained("indobertweet-fine-tuned") |
| | tokenizer = AutoTokenizer.from_pretrained("indolem/indobertweet-base-uncased") |
| | classifier = pipeline('text-classification', model=model, tokenizer=tokenizer) |
| |
|
| | def classify(text): |
| | text = text.strip().lower() |
| | result = classifier(text) |
| | yhat = result[0]['label'] |
| | return result |
| | |
| | |
| |
|
| | import gradio as gr |
| |
|
| | iface = gr.Interface( |
| | fn=classify, |
| | inputs=[ |
| | gr.Textbox(placeholder="Lewandowski bermain buruk sekali, Xavi benar-benar marah kepadanya", label="Enter text to classify emotions", lines=5) |
| | ], |
| | outputs=gr.Textbox(label="Classification Result"), |
| | title="🔮 Emotion Classification", |
| | description="Enter a text and classify its emotions." |
| | ) |
| | iface.launch() |