Instructions to use oliverqq/scibert-uncased-topics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oliverqq/scibert-uncased-topics with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oliverqq/scibert-uncased-topics")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oliverqq/scibert-uncased-topics") model = AutoModelForSequenceClassification.from_pretrained("oliverqq/scibert-uncased-topics") - Notebooks
- Google Colab
- Kaggle
updated file location
Browse files- scibert-title-topics/config.json β config.json +0 -0
- scibert-title-topics/pytorch_model.bin β pytorch_model.bin +0 -0
- scibert-title-topics/special_tokens_map.json β special_tokens_map.json +0 -0
- scibert-title-topics/tokenizer_config.json β tokenizer_config.json +0 -0
- scibert-title-topics/vocab.txt β vocab.txt +0 -0
scibert-title-topics/config.json β config.json
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scibert-title-topics/pytorch_model.bin β pytorch_model.bin
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scibert-title-topics/special_tokens_map.json β special_tokens_map.json
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scibert-title-topics/tokenizer_config.json β tokenizer_config.json
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scibert-title-topics/vocab.txt β vocab.txt
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