Download model during runtime
Browse files- README.md +2 -2
- app.py +15 -0
- requirements.txt +2 -1
README.md
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license: cc-by-4.0
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python_version: 3.12
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short_description: KazSandra project @ ISSAI
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preload_from_hub:
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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license: cc-by-4.0
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python_version: 3.12
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short_description: KazSandra project @ ISSAI
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#preload_from_hub:
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#- "issai/rembert-sentiment-analysis-polarity-classification-kazakh"
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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@@ -12,3 +12,18 @@ login(os.getenv('HF_TOKEN'))
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hub_folder = Path('~/.cache/huggingface/hub')
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for path in hub_folder.walk():
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st.write(path)
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hub_folder = Path('~/.cache/huggingface/hub')
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for path in hub_folder.walk():
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st.write(path)
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from transformers import AutoModelForSequenceClassification
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from transformers import AutoTokenizer
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from transformers import TextClassificationPipeline
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model = AutoModelForSequenceClassification.from_pretrained(
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"issai/rembert-sentiment-analysis-polarity-classification-kazakh")
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tokenizer = AutoTokenizer.from_pretrained("issai/rembert-sentiment-analysis-polarity-classification-kazakh")
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pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)
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reviews = ["Бұл бейнефильм маған түк ұнамады.", "Осы кітап қызық сияқты."]
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for review in reviews:
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st.write(pipe(review))
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requirements.txt
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streamlit~=1.37.0
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streamlit~=1.37.0
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huggingface-hub~=0.24.5
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