Instructions to use ctoraman/deprem-mdeberta-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ctoraman/deprem-mdeberta-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ctoraman/deprem-mdeberta-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ctoraman/deprem-mdeberta-ner") model = AutoModelForTokenClassification.from_pretrained("ctoraman/deprem-mdeberta-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- caf1923afd036fa2197da43cdf8bb3868abf34e37174508a0698bb3489c13a4d
- Size of remote file:
- 16.3 MB
- SHA256:
- 6f06fc3bbcbbc8f07be861b9a6a69e177247bc549b2bcbf0483e4dd98c06b6f6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.