Instructions to use abidlabs/trackio-demo-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abidlabs/trackio-demo-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="abidlabs/trackio-demo-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("abidlabs/trackio-demo-model") model = AutoModelForSequenceClassification.from_pretrained("abidlabs/trackio-demo-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 043b53e1619420285055faa0a90fe9c6336b809fbcab2273e098f1fe4235ee0e
- Size of remote file:
- 5.2 kB
- SHA256:
- 127c5a91674d07619f554903160860c2b4cef8f7199ae3de5ce26972a691249a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.