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fasttext_climate.bin - Trained FastText modelchunk_labels.jsonl - Training data (220K labeled chunks)fasttext_train.txt - Training filefasttext_valid.txt - Validation filekeywords.txt - Climate/nature keywordsevaluation_metrics.json - Full metricstraining_config.json - Training configuration1import fasttext
2from fasttext.FastText import _FastText as FastTextModel
3
4# NumPy 2.x compatibility patch
5def patched_predict(self, text, k=1, threshold=0.0, on_unicode_error='strict'):
6 import warnings
7 with warnings.catch_warnings():
8 warnings.simplefilter("ignore")
9 result = self.f.predict(text, k, threshold, on_unicode_error)
10 if result:
11 probs = [float(p) for p, _ in result]
12 labels = [l for _, l in result]
13 return tuple(labels), probs
14 else:
15 return (), []
16
17FastTextModel.predict = patched_predict
18
19# Load model
20model = fasttext.load_model('fasttext_climate.bin')
21
22# Predict
23labels, probs = model.predict('carbon emissions and global warming')
24print(f'Label: {labels[0]}, Probability: {probs[0]:.4f}')