Instructions to use ruanchaves/mdeberta-v3-base-hatebr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ruanchaves/mdeberta-v3-base-hatebr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ruanchaves/mdeberta-v3-base-hatebr")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ruanchaves/mdeberta-v3-base-hatebr") model = AutoModelForSequenceClassification.from_pretrained("ruanchaves/mdeberta-v3-base-hatebr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ruanchaves/mdeberta-v3-base-hatebr: direct link, hf CLI and curl.
- Browser
- Download file 3.7 kB
-
https://huggingface.co/ruanchaves/mdeberta-v3-base-hatebr/resolve/main/training_args.bin
- Command line
-
hf download hf://ruanchaves/mdeberta-v3-base-hatebr/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ruanchaves/mdeberta-v3-base-hatebr/resolve/main/training_args.bin
3.7 kB
- Xet hash:
- 620d5c43d6a85fd995c75e52645a6ad290811bc154871cf47821175857280853
- Size of remote file:
- 3.7 kB
- SHA256:
- ed1188fb4cfa97f577935299afa4fa67ffa047a1fc18035065dbd8b21dda53e8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.