Spaces:
Sleeping
Sleeping
| from datasets import load_dataset | |
| from evaluate import evaluator | |
| from transformers import AutoModelForSequenceClassification, pipeline, AutoTokenizer | |
| import gradio as gr | |
| data = load_dataset("huolongguo10/check_sec_eval",split="test") | |
| task_evaluator = evaluator("text-classification") | |
| model = AutoModelForSequenceClassification.from_pretrained("huolongguo10/check_sec") | |
| tokenizer = AutoTokenizer.from_pretrained("huolongguo10/check_sec") | |
| model_tiny = AutoModelForSequenceClassification.from_pretrained("huolongguo10/check_sec_tiny") | |
| tokenizer_tiny = AutoTokenizer.from_pretrained("huolongguo10/check_sec_tiny") | |
| # 1. Pass a model name or path | |
| eval_results = task_evaluator.compute( | |
| model_or_pipeline=model, | |
| data=data, | |
| input_column="sentence1", | |
| label_mapping={"LABEL_0": 0, "LABEL_1": 1}, | |
| tokenizer=tokenizer | |
| ) | |
| eval_results_tiny = task_evaluator.compute( | |
| model_or_pipeline=model_tiny, | |
| data=data, | |
| input_column="sentence1", | |
| label_mapping={"LABEL_0": 0, "LABEL_1": 1}, | |
| tokenizer=tokenizer_tiny | |
| ) | |
| with gr.Blocks() as demo: | |
| gr.Markdown('# Base:') | |
| gr.JSON(eval_results) | |
| gr.Markdown('# Tiny:') | |
| gr.JSON(eval_results_tiny) | |
| print(eval_results) | |
| demo.launch() |