Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use netaicsco/v1_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use netaicsco/v1_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="netaicsco/v1_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("netaicsco/v1_classifier") model = AutoModelForSequenceClassification.from_pretrained("netaicsco/v1_classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7edc3865f374a0a4f8e1de254fdaf933e8bd521d5f5a11aafea382d76ea54b03
- Size of remote file:
- 268 MB
- SHA256:
- 8dabc2c947adb7d7e6c281128ffd3dd68e702fc4adc1446a933c86e1ae89d832
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.