Instructions to use KBLab/megatron.bert-large.unigram-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-large.unigram-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-large.unigram-32k-pretok.25k-steps")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KBLab/megatron.bert-large.unigram-32k-pretok.25k-steps") model = AutoModel.from_pretrained("KBLab/megatron.bert-large.unigram-32k-pretok.25k-steps") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4717fe65b1231684d1817015a487257bd28e1c299442dcf853aeb26730805bb
- Size of remote file:
- 1.35 GB
- SHA256:
- ebf5b6c14b70e5a9914747e191dc2593250d57fe61954af70c39fc63b0e4760c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.