Text Classification
Transformers
PyTorch
TensorBoard
ONNX
Safetensors
English
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use usvsnsp/code-vs-nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use usvsnsp/code-vs-nl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="usvsnsp/code-vs-nl")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("usvsnsp/code-vs-nl") model = AutoModelForSequenceClassification.from_pretrained("usvsnsp/code-vs-nl") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 78df7c7d2363aacfbdd34e9dc6b771ef87919009cc17fa0035bc8b215e209a3c
- Size of remote file:
- 3.45 kB
- SHA256:
- 5bfad0bbe4116f7ab212fd1b22ffa8d183762d55fd81bf4d3122633c3b7bf957
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.