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 model.safetensors from Ahmedjr/codebert-algorithm-tagger: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Ahmedjr/codebert-algorithm-tagger/resolve/main/model.safetensors
- Command line
-
hf download hf://Ahmedjr/codebert-algorithm-tagger/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Ahmedjr/codebert-algorithm-tagger/resolve/main/model.safetensors
499 MB
- Xet hash:
- 8ee78794cbf32ca3753069b62215d29d1c46a4da790f7a093e04848cd82992e7
- Size of remote file:
- 499 MB
- SHA256:
- 63b915d7972ca27ab314f05e097d81d7e95935f6c628e6f3b10dd4e37f8707b8
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