Text Classification
Transformers
PyTorch
Safetensors
English
bert
nlp
code
classification
text-embeddings-inference
Instructions to use TigerByteCyber/codenlbert-sm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TigerByteCyber/codenlbert-sm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TigerByteCyber/codenlbert-sm")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TigerByteCyber/codenlbert-sm") model = AutoModelForSequenceClassification.from_pretrained("TigerByteCyber/codenlbert-sm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fff1962d93fe636b980141149742df0621a4b58f2f4e015900dd7b57bbddd842
- Size of remote file:
- 3.96 kB
- SHA256:
- d56d514bdc2742c6ac3d0562fd500b82e21ecbb212313b29d1307933e5fb6529
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