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Update app.py
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app.py
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@@ -28,9 +28,54 @@ logits = model(input_ids, attention_mask)[0]
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probs = torch.softmax(logits, dim=1)
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predicted_category = torch.argmax(probs).item()
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# from transformers import pipeline
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# pipe = pipeline("ner", "Davlan/distilbert-base-multilingual-cased-ner-hrl")
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raw_predictions = predicted_category#le.inverse_transform(prediction)#pipe(text)
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# тут уже знакомый вам код с huggingface.transformers -- его можно заменить на что угодно от fairseq до catboost
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st.markdown(f"{raw_predictions}")
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probs = torch.softmax(logits, dim=1)
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predicted_category = torch.argmax(probs).item()
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tags_names = ['acc-phys',
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'adap-org',
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"adap-org'",
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'alg-geom',
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'astro-ph',
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"astro-ph'",
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'chao-dyn',
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'chem-ph',
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'cmp-lg',
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"cmp-lg'",
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'comp-gas',
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'cond-mat',
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"cond-mat'",
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'cs',
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'dg-ga',
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'econ',
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'eess',
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'funct-an',
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'gr-qc',
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"gr-qc'",
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'hep-ex',
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"hep-ex'",
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'hep-lat',
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"hep-lat'",
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'hep-ph',
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"hep-ph'",
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'hep-th',
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"hep-th'",
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'math',
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'math-ph',
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'mtrl-th',
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'nlin',
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'nucl-ex',
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'nucl-th',
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"nucl-th'",
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'patt-sol',
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'physics',
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'q-alg',
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'q-bio',
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'q-fin',
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'quant-ph',
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"quant-ph'",
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'solv-int',
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'stat']
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# from transformers import pipeline
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# pipe = pipeline("ner", "Davlan/distilbert-base-multilingual-cased-ner-hrl")
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raw_predictions = tags_names[predicted_category]#le.inverse_transform(prediction)#pipe(text)
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# тут уже знакомый вам код с huggingface.transformers -- его можно заменить на что угодно от fairseq до catboost
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st.markdown(f"{raw_predictions}")
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