Instructions to use NbAiLabArchive/test_w5_long_roberta_tokenizer_adafactor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/test_w5_long_roberta_tokenizer_adafactor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLabArchive/test_w5_long_roberta_tokenizer_adafactor")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLabArchive/test_w5_long_roberta_tokenizer_adafactor") model = AutoModelForMaskedLM.from_pretrained("NbAiLabArchive/test_w5_long_roberta_tokenizer_adafactor") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4356180c1e804b0c522b5f3a94e269ebe5155614f45fde46a489b03111abe955
- Size of remote file:
- 499 MB
- SHA256:
- b9a3df3717d43c21dafab5dc1d72e983b827413fbd4bcbc71568326740208d97
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