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:
- d80e5b396e0e7eab58088febfa1e90acc8c9e98a57c5d07e4ebd46b874506116
- Size of remote file:
- 436 MB
- SHA256:
- e7ff9a607bca83c520df04906ea08b6f280584382d6d3fbdcdf3762eeaf45fc0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.