Instructions to use DeepPavlov/rubert-base-cased-conversational with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepPavlov/rubert-base-cased-conversational with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DeepPavlov/rubert-base-cased-conversational")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DeepPavlov/rubert-base-cased-conversational") model = AutoModel.from_pretrained("DeepPavlov/rubert-base-cased-conversational") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9739b2f1b4c697ec7a7d82d47f1547398c32e513376e789b477ff65eb61d9e46
- Size of remote file:
- 714 MB
- SHA256:
- 531caf551269b5ac347d79c49cf387d1953bd24b85d31ef90db56b0b9543aff7
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