Instructions to use jamesLeeeeeee/code-search-net-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jamesLeeeeeee/code-search-net-tokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jamesLeeeeeee/code-search-net-tokenizer")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jamesLeeeeeee/code-search-net-tokenizer") model = AutoModelForTokenClassification.from_pretrained("jamesLeeeeeee/code-search-net-tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1784b64b72faa2305dd68bafeef14e423e237bc54b5b9a6070bcfe359936900f
- Size of remote file:
- 4.92 kB
- SHA256:
- 2ad6319310a7132b1780ad108d76a0709fbef5f03ca21636fd87ffddc95e7c71
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