Instructions to use aychang/bert-base-cased-trec-coarse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aychang/bert-base-cased-trec-coarse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aychang/bert-base-cased-trec-coarse")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aychang/bert-base-cased-trec-coarse") model = AutoModelForSequenceClassification.from_pretrained("aychang/bert-base-cased-trec-coarse") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 290bc38f45ce6ffce3a2fbd173c58d333befea77eacecd73f95d64892fc97a40
- Size of remote file:
- 433 MB
- SHA256:
- 6b8279250ea5ece6f7f63476aed6ebd2405a447d095b7b2e55047b4855efbb6b
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