Text Classification
Transformers
Safetensors
English
deberta-v2
deberta
sequence-classification
medicine
telerehabilitation
MeDeBERTa
MeDeBERTaBot
Instructions to use malakhovks/MeDeBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use malakhovks/MeDeBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="malakhovks/MeDeBERTa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("malakhovks/MeDeBERTa") model = AutoModelForSequenceClassification.from_pretrained("malakhovks/MeDeBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from malakhovks/MeDeBERTa: direct link, hf CLI and curl.
- Browser
- Download file 5.71 kB
-
https://huggingface.co/malakhovks/MeDeBERTa/resolve/main/training_args.bin
- Command line
-
hf download hf://malakhovks/MeDeBERTa/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/malakhovks/MeDeBERTa/resolve/main/training_args.bin
5.71 kB
- Xet hash:
- 65caafb93a35c5622696c9c9f84933eba412c51670a697afcd145c8e133e53b0
- Size of remote file:
- 5.71 kB
- SHA256:
- cf3f6bdff1f26ed479f70a4c50c0079d364eefb37e4e306f42ca7a97ac497df0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.