Text Classification
Transformers
Safetensors
English
roberta
code
algorithms
competitive-programming
multi-label-classification
codebert
text-embeddings-inference
Instructions to use Ahmedjr/codebert-algorithm-tagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ahmedjr/codebert-algorithm-tagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ahmedjr/codebert-algorithm-tagger")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ahmedjr/codebert-algorithm-tagger") model = AutoModelForSequenceClassification.from_pretrained("Ahmedjr/codebert-algorithm-tagger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Ahmedjr/codebert-algorithm-tagger: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/Ahmedjr/codebert-algorithm-tagger/resolve/main/training_args.bin
- Command line
-
hf download hf://Ahmedjr/codebert-algorithm-tagger/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ahmedjr/codebert-algorithm-tagger/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- bda077f5e2902166d4f30c60ec109f7794ca50eb5b09930709ae9c64dd85591b
- Size of remote file:
- 5.84 kB
- SHA256:
- ff0f7232072f05c17bf6222f67fb04980789d2548781dadaf9c3b0748d02fa05
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