Instructions to use NbAiLabArchive/test_w5_long_roberta_tokenizer 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 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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLabArchive/test_w5_long_roberta_tokenizer") model = AutoModelForMaskedLM.from_pretrained("NbAiLabArchive/test_w5_long_roberta_tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from NbAiLabArchive/test_w5_long_roberta_tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/NbAiLabArchive/test_w5_long_roberta_tokenizer/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://NbAiLabArchive/test_w5_long_roberta_tokenizer@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NbAiLabArchive/test_w5_long_roberta_tokenizer/resolve/refs%2Fpr%2F1/pytorch_model.bin
499 MB
- Xet hash:
- 2293ae1eff923d2ac67d90d5514b134b6a4acda64e5e386350ea9195d61c1b81
- Size of remote file:
- 499 MB
- SHA256:
- b54e2cd65c99908d72fff0e170bed8207a70ae3f9acd8c1384206342179914f5
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