Token Classification
Transformers
French
German
ocr_qa_assessment
ocr
bloomfilter
unigram
impresso
quality-assessment
v1.0.6
custom_code
Instructions to use impresso-project/ocr-quality-assessor-unigram-light with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use impresso-project/ocr-quality-assessor-unigram-light with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="impresso-project/ocr-quality-assessor-unigram-light", trust_remote_code=True)# Load model directly from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("impresso-project/ocr-quality-assessor-unigram-light", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update modeling_ocrqa.py
Browse files- modeling_ocrqa.py +1 -1
modeling_ocrqa.py
CHANGED
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@@ -94,7 +94,7 @@ class QAAssessmentModel(PreTrainedModel):
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self.ocrqa_assessors[lang] = self.get_bloomfilter(model_id=self.config.config._name_or_path,
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filename=model_filename)
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-
print(self.ocrqa_assessors)
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self.lang_pipeline = pipeline("langident",
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model="impresso-project/language-identifier",
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trust_remote_code=True,
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self.ocrqa_assessors[lang] = self.get_bloomfilter(model_id=self.config.config._name_or_path,
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filename=model_filename)
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+
# print(self.ocrqa_assessors)
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self.lang_pipeline = pipeline("langident",
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model="impresso-project/language-identifier",
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trust_remote_code=True,
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