Instructions to use Dauka-transformers/BERT_word2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dauka-transformers/BERT_word2vec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Dauka-transformers/BERT_word2vec")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Dauka-transformers/BERT_word2vec") model = AutoModelForMaskedLM.from_pretrained("Dauka-transformers/BERT_word2vec") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5298ab90f06b913cea3ae6f314e8a068597d2bf408f1063b8d07517a783841b
- Size of remote file:
- 1.36 GB
- SHA256:
- 0ac309ff6f5569b658df47ac2061d6bf4e8f9de1dd6c16defa75b1a2440d6c08
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