Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use superone001/my-test-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use superone001/my-test-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="superone001/my-test-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("superone001/my-test-model") model = AutoModelForSequenceClassification.from_pretrained("superone001/my-test-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2fec0251dbe539272ad315c38b7c1f523587a157e58f8f7ac6f0e9dec4d5746d
- Size of remote file:
- 5.78 kB
- SHA256:
- b813a4b1c0488055858fd4ac2afca52f642a354452729f7b278a1b055197cc10
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.