Instructions to use crcb/hs_dvs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crcb/hs_dvs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="crcb/hs_dvs")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("crcb/hs_dvs") model = AutoModelForSequenceClassification.from_pretrained("crcb/hs_dvs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a1abadc22e1cf46aa9045b76d463e4f0eab2e087756114f623561643865a32f
- Size of remote file:
- 3.06 kB
- SHA256:
- 40629402906fd251f0e314687b22b1ac9a9bef8fbae65412b85ecd9391035139
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.