Instructions to use Mozart-coder/BERT_full_data3-6_tokenized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mozart-coder/BERT_full_data3-6_tokenized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mozart-coder/BERT_full_data3-6_tokenized")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Mozart-coder/BERT_full_data3-6_tokenized") model = AutoModelForMaskedLM.from_pretrained("Mozart-coder/BERT_full_data3-6_tokenized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca7136693023874cc986d763ef5a53f2aa36c0d116d193dbe0b3e92d55979112
- Size of remote file:
- 3.44 kB
- SHA256:
- 63916d352843f5f26c8e995ae7ed5ae9d2219d5a246323925174deed83e7141d
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