Token Classification
Transformers
PyTorch
English
deberta-v2
text-classification
deberta-v3-base
deberta-v3
deberta
emotion
Instructions to use akira225/deberta-v3-base-ECE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akira225/deberta-v3-base-ECE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="akira225/deberta-v3-base-ECE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("akira225/deberta-v3-base-ECE") model = AutoModelForSequenceClassification.from_pretrained("akira225/deberta-v3-base-ECE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from akira225/deberta-v3-base-ECE: direct link, hf CLI and curl.
- Browser
- Download file 735 MB
-
https://huggingface.co/akira225/deberta-v3-base-ECE/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://akira225/deberta-v3-base-ECE/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/akira225/deberta-v3-base-ECE/resolve/main/pytorch_model.bin
735 MB
- Xet hash:
- 141cd9c92797afad023bed376d3d069226228a4b7d47c29c869bcc6a04a9fac4
- Size of remote file:
- 735 MB
- SHA256:
- 37e38296c6506c5a778fd5600e6076a475b4a77c62aeba9cdf0d175fe67c3575
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