Instructions to use DimasikKurd/longformer_pos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DimasikKurd/longformer_pos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="DimasikKurd/longformer_pos")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("DimasikKurd/longformer_pos") model = AutoModelForTokenClassification.from_pretrained("DimasikKurd/longformer_pos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57d463d9d32de9f0a6cb6d0b53926c39fb72d83c79133329fc5de02cd4153b33
- Size of remote file:
- 17.1 MB
- SHA256:
- 06a1396e1ff4e78518af44b7521d9e5b9c6219fed36303610d202b685bab9782
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