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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8468aa34e21452cd0b9580d48996d87c3cd710857a6701f8961a45fa68965966
- Size of remote file:
- 19.5 MB
- SHA256:
- 77743a0923b26a8b87ed0d8d8f7834e33c817308658697f35a93d8c2668af7d0
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