Instructions to use DeepPavlov/bert-base-cased-conversational with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepPavlov/bert-base-cased-conversational with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DeepPavlov/bert-base-cased-conversational")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DeepPavlov/bert-base-cased-conversational") model = AutoModel.from_pretrained("DeepPavlov/bert-base-cased-conversational") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4bd370e84557e72f73f7abfaba831cfd9791fe5bb379f5b910ce15c45bd23c7e
- Size of remote file:
- 436 MB
- SHA256:
- 58656f54d29a94eb09ac30516af19135aa930b31d6cd64b2481172396758e838
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