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")# 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") - Notebooks
- Google Colab
- Kaggle
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README.md
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| macro F1 | **0.9206** |
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| accuracy | 0.9306 |
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Head-to-head vs `philomath-1209/programming-language-identification` on the 26
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labels both models support (3,057 test rows):
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| **this model** | **0.9444** | **0.9636** |
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| philomath-1209 | 0.8449 | 0.8445 |
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Wins on every shared label. Largest gaps: ARM Assembly +0.354, Erlang +0.270,
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COBOL +0.216, Pascal +0.206, Fortran +0.193, Mathematica/Wolfram +0.173.
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| macro F1 | **0.9206** |
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| accuracy | 0.9306 |
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Wins on every shared label. Largest gaps: ARM Assembly +0.354, Erlang +0.270,
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COBOL +0.216, Pascal +0.206, Fortran +0.193, Mathematica/Wolfram +0.173.
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