Instructions to use jaggernaut007/roberta-base-finetuned-abbr-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaggernaut007/roberta-base-finetuned-abbr-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jaggernaut007/roberta-base-finetuned-abbr-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jaggernaut007/roberta-base-finetuned-abbr-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("jaggernaut007/roberta-base-finetuned-abbr-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3498eddcd989c03470b9811952ff2824e0440f0684247576a5f6641e8803c8b3
- Size of remote file:
- 4.98 kB
- SHA256:
- cf04021f522f7b43f71fefd316d8c0ca5b5751e7f8a24ac1e80c43c0137fe786
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.