Instructions to use muratti18462/debertabase_term_paper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muratti18462/debertabase_term_paper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="muratti18462/debertabase_term_paper", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("muratti18462/debertabase_term_paper") model = AutoModelForTokenClassification.from_pretrained("muratti18462/debertabase_term_paper", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8f4f7969178e2f8c8fb18e7f3e965b29ab95e3658f4c97861f3fd463f7aff6f
- Size of remote file:
- 5.37 kB
- SHA256:
- bbd03987ef88089c0364f9d28ef6c28df1ebc9af17b55be21ab4a955f58b16ba
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