Instructions to use Yash22CSU192/davidson-roberta-hatespeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yash22CSU192/davidson-roberta-hatespeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Yash22CSU192/davidson-roberta-hatespeech")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Yash22CSU192/davidson-roberta-hatespeech") model = AutoModelForSequenceClassification.from_pretrained("Yash22CSU192/davidson-roberta-hatespeech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5e9ec9fc5ad6d83717b2b093bca2a04faea226e4ad685c5d6379872b924e58d
- Size of remote file:
- 5.3 kB
- SHA256:
- c138858acddebc19ad8efcd62a7a229801ee0e2c14a9ea774d565421b9b83266
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.