Token Classification
Transformers
PyTorch
TensorBoard
deberta
Generated from Trainer
Eval Results (legacy)
Instructions to use geckos/deberta-base-fine-tuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use geckos/deberta-base-fine-tuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="geckos/deberta-base-fine-tuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("geckos/deberta-base-fine-tuned-ner") model = AutoModelForTokenClassification.from_pretrained("geckos/deberta-base-fine-tuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from geckos/deberta-base-fine-tuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 555 MB
-
https://huggingface.co/geckos/deberta-base-fine-tuned-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://geckos/deberta-base-fine-tuned-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/geckos/deberta-base-fine-tuned-ner/resolve/main/pytorch_model.bin
555 MB
- Xet hash:
- d141cf68700e9c7a60116290b5c89a2590ab844a41c994e0cc7b91e2c828a6af
- Size of remote file:
- 555 MB
- SHA256:
- 348f665c3b82fc31c48333cdd3901dcd2e56c67062daed40b8f3ed6065b32e94
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.