Instructions to use Mozart-coder/BERT_Feb-6_tokenized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mozart-coder/BERT_Feb-6_tokenized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mozart-coder/BERT_Feb-6_tokenized")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Mozart-coder/BERT_Feb-6_tokenized") model = AutoModelForMaskedLM.from_pretrained("Mozart-coder/BERT_Feb-6_tokenized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74571b918315bad3530eefdcda0cdaa0d93c4ef68766b17815e53270690ed01c
- Size of remote file:
- 3.38 kB
- SHA256:
- 8f33104cd9b3e2f2fb1ffc93e6f72bebcd3eab8a3c0af319e9102fe1b04998e7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.