Instructions to use amrithanandini/bert-relay-decoding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amrithanandini/bert-relay-decoding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="amrithanandini/bert-relay-decoding")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("amrithanandini/bert-relay-decoding") model = AutoModelForTokenClassification.from_pretrained("amrithanandini/bert-relay-decoding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2279b07c3f55d0e20fafe03b013cbb3fdb60449006b1eefc074212a4ea08fc8
- Size of remote file:
- 871 MB
- SHA256:
- e9490a5287f5a45d68e6cd181b2373070d9833cc7dd8ad3221ace7a226c6dd71
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