Instructions to use Mozart-coder/BERT_newdata-6_tokenized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mozart-coder/BERT_newdata-6_tokenized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mozart-coder/BERT_newdata-6_tokenized")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Mozart-coder/BERT_newdata-6_tokenized") model = AutoModelForMaskedLM.from_pretrained("Mozart-coder/BERT_newdata-6_tokenized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a26ea06ec85191b2333ef53ffed6239fc40a470f41bf95d44699508cf69750cd
- Size of remote file:
- 3.38 kB
- SHA256:
- 6f71cea9378274afa4f43ef77d26dfd844d83e15cc3a553c657a33e55f2493d6
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