Text Classification
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
trl
reward-trainer
Instructions to use nteku1/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nteku1/test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nteku1/test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nteku1/test") model = AutoModelForSequenceClassification.from_pretrained("nteku1/test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a03891bdf6d90cef50ad19915d217425e142a455af1b9afa30a8c3bfb34014da
- Size of remote file:
- 5.3 kB
- SHA256:
- f560d62680da2aeb396f982b39f0877f1dc3f5a37c754561e7b2ca8275bf6b26
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