Instructions to use NetworkIsLife/vad-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NetworkIsLife/vad-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NetworkIsLife/vad-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NetworkIsLife/vad-bert") model = AutoModelForSequenceClassification.from_pretrained("NetworkIsLife/vad-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8fdcdcb89b3ed2ba9c4fcd69bee1aba11d6f6825401fed204077f830e6adbcee
- Size of remote file:
- 438 MB
- SHA256:
- 72b2f808e584d97441af84baf14819cc71eb3ec7f8019b1c1f8c2f5cfba9b0e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.