Instructions to use warrior1127/hate_bert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use warrior1127/hate_bert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="warrior1127/hate_bert_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("warrior1127/hate_bert_model") model = AutoModelForSequenceClassification.from_pretrained("warrior1127/hate_bert_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5190a62a4055285cf986cf3a45c9c70c5a133643b04e259609e4f3c3f5a478f0
- Size of remote file:
- 433 MB
- SHA256:
- 4bad6b1b03d8c512c2560676f6b1ba414d6f9c66e0b3e9e0d8fa612d476f4c9e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.