Text Classification
Transformers
ONNX
Safetensors
modernbert
code
programming-language-identification
language-detection
text-embeddings-inference
Instructions to use FrameByFrame/programming-language-identification-100plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FrameByFrame/programming-language-identification-100plus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FrameByFrame/programming-language-identification-100plus")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FrameByFrame/programming-language-identification-100plus") model = AutoModelForSequenceClassification.from_pretrained("FrameByFrame/programming-language-identification-100plus", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download .gitattributes from FrameByFrame/programming-language-identification-100plus: direct link, hf CLI and curl.
- Browser
- Download file 144 Bytes
-
https://huggingface.co/FrameByFrame/programming-language-identification-100plus/resolve/main/.gitattributes
- Command line
-
hf download hf://FrameByFrame/programming-language-identification-100plus/.gitattributes
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curl -L -o .gitattributes https://huggingface.co/FrameByFrame/programming-language-identification-100plus/resolve/main/.gitattributes
144 Bytes
| *.safetensors filter=lfs diff=lfs merge=lfs -text | |
| *.bin filter=lfs diff=lfs merge=lfs -text | |
| onnx/model.onnx filter=lfs diff=lfs merge=lfs -text | |