Instructions to use KBLab/megatron.bert-base.wordpiece-32k-pretok.25k-steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/megatron.bert-base.wordpiece-32k-pretok.25k-steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="KBLab/megatron.bert-base.wordpiece-32k-pretok.25k-steps")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KBLab/megatron.bert-base.wordpiece-32k-pretok.25k-steps") model = AutoModel.from_pretrained("KBLab/megatron.bert-base.wordpiece-32k-pretok.25k-steps") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59ea13ac37cbdf79c1cc36d7d663dda8fc686b06c4daf191c43ea3de74a8a85f
- Size of remote file:
- 442 MB
- SHA256:
- 38c1876e45c68e39de750ba62d3f0741e4848c2718b683d73b162636fe4e36b1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.