Text Classification
Transformers
PyTorch
TensorBoard
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use SiddharthaM/mdeberta-hate-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SiddharthaM/mdeberta-hate-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SiddharthaM/mdeberta-hate-final")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SiddharthaM/mdeberta-hate-final") model = AutoModelForSequenceClassification.from_pretrained("SiddharthaM/mdeberta-hate-final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spm.model from SiddharthaM/mdeberta-hate-final: direct link, hf CLI and curl.
- Browser
- Download file 4.31 MB
-
https://huggingface.co/SiddharthaM/mdeberta-hate-final/resolve/main/spm.model
- Command line
-
hf download hf://SiddharthaM/mdeberta-hate-final/spm.model
-
curl -L -o spm.model https://huggingface.co/SiddharthaM/mdeberta-hate-final/resolve/main/spm.model
4.31 MB
- Xet hash:
- c4b914ec5ab295fbee3b0e0fe62f22febb3c09d34dd3778745e0de034a76df33
- Size of remote file:
- 4.31 MB
- SHA256:
- 13c8d666d62a7bc4ac8f040aab68e942c861f93303156cc28f5c7e885d86d6e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.