Instructions to use marcchew/testv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcchew/testv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcchew/testv3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marcchew/testv3") model = AutoModelForSequenceClassification.from_pretrained("marcchew/testv3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 775fb7c7c8a538d6340a8d9a2c902e7c11253fe8f78a13dbcb54e6585f55911a
- Size of remote file:
- 5.18 kB
- SHA256:
- d37058e5266eda27c458f60bd6874218ab59a39868d0bda74ade2547b51d151d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.