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100 lines
2.6 KiB
100 lines
2.6 KiB
# SPDX-License-Identifier: LGPL-3.0-or-later |
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import gptc.tokenizer, gptc.compiler, gptc.exceptions |
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import warnings |
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class Classifier: |
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"""A text classifier. |
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Parameters |
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---------- |
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model : dict |
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A compiled GPTC model. |
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Attributes |
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---------- |
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model : dict |
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The model used. |
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""" |
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def __init__(self, model): |
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try: |
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model_version = model["__version__"] |
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except: |
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model_version = 1 |
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if model_version == 3: |
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self.model = model |
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else: |
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# The model is an unsupported version |
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try: |
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raw_model = model["__raw__"] |
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except: |
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raise gptc.exceptions.UnsupportedModelError( |
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"this model is unsupported and does not contain a raw model for recompiling" |
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) |
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warnings.warn( |
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"model needed to be recompiled on-the-fly; please re-compile it and use the new compiled model in the future" |
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) |
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self.model = gptc.compiler.compile(raw_model) |
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def confidence(self, text): |
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"""Classify text with confidence. |
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Parameters |
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---------- |
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text : str |
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The text to classify |
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Returns |
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------- |
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dict |
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{category:probability, category:probability...} or {} if no words |
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matching any categories in the model were found |
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""" |
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model = self.model |
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text = gptc.tokenizer.tokenize(text) |
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probs = {} |
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for word in text: |
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try: |
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for category, value in enumerate(model[word]): |
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try: |
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probs[category] += value |
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except KeyError: |
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probs[category] = value |
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except KeyError: |
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pass |
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probs = { |
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model["__names__"][category]: value / 65535 |
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for category, value in probs.items() |
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} |
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total = sum(probs.values()) |
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probs = {category: value / total for category, value in probs.items()} |
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return probs |
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def classify(self, text): |
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"""Classify text. |
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Parameters |
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---------- |
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text : str |
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The text to classify |
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Returns |
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------- |
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str or None |
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The most likely category, or None if no words matching any |
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category in the model were found. |
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""" |
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probs = self.confidence(text) |
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try: |
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return sorted(probs.items(), key=lambda x: x[1])[-1][0] |
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except IndexError: |
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return None
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