Instructions to use danielbubiola/fine_tuned_text_clf_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danielbubiola/fine_tuned_text_clf_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="danielbubiola/fine_tuned_text_clf_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("danielbubiola/fine_tuned_text_clf_model") model = AutoModelForSequenceClassification.from_pretrained("danielbubiola/fine_tuned_text_clf_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d7c87a9d241cb702278e6de04aba9636f08434ebb7f26dfef3091318c52ef414
- Size of remote file:
- 268 MB
- SHA256:
- bf5530dbf2203aaf0aa7eead1f412dac90a3aef2642d5d7c141a65f2a8f97774
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.