Instructions to use mrm8488/codebert-base-finetuned-code-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/codebert-base-finetuned-code-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mrm8488/codebert-base-finetuned-code-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mrm8488/codebert-base-finetuned-code-ner") model = AutoModelForTokenClassification.from_pretrained("mrm8488/codebert-base-finetuned-code-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a8e0b0df9949e18467da1f5d07667a6ceba04caa2c87892a838000f947977a7
- Size of remote file:
- 3.44 kB
- SHA256:
- f07c8117a04d8bf46cd0be4d94369a2124575cecfea1a3c04c528784e0b03cdb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.