| import fasttext | |
| from transformers import pipeline | |
| class FastTextClassifierPipeline(pipeline): | |
| def __init__(self, model_path): | |
| self.model = fasttext.load_model(model_path) | |
| def __call__(self, texts): | |
| if isinstance(texts, str): | |
| texts = [texts] | |
| results = [] | |
| for text in texts: | |
| prediction = self.model.predict(text) | |
| label = prediction[0][0].replace("__label__", "") | |
| score = prediction[1][0] | |
| results.append({"label": label, "score": score}) | |
| return results | |
| classifier = FastTextClassifierPipeline("fasttext_news_classifier.bin") |