Instructions to use phunganhsang/model_content_V2_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phunganhsang/model_content_V2_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="phunganhsang/model_content_V2_test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("phunganhsang/model_content_V2_test") model = AutoModelForSequenceClassification.from_pretrained("phunganhsang/model_content_V2_test") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e7e493eb2fc926503573c5a05ca6e5a585765e63cbdbf6e3a9a10d5ff64285a1
- Size of remote file:
- 5.3 kB
- SHA256:
- b398caafc44545811b2935a802ab1d358cabd951e091504cf487936427f8a10f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.