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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLabArchive/test_w5_long_roberta_tokenizer_adafactor: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/NbAiLabArchive/test_w5_long_roberta_tokenizer_adafactor/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLabArchive/test_w5_long_roberta_tokenizer_adafactor/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLabArchive/test_w5_long_roberta_tokenizer_adafactor/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- f3d34c6975274b552304b49a15d0ec490ff31860694ea48ad15ee2f4777b8e37
- Size of remote file:
- 499 MB
- SHA256:
- bd6f9d6dd5f8e9fd782da66793fe85cee314f43a45a1cd59aa6db0ea58077ac7
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