Instructions to use tsdocode/phobert-finetune-hatespeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tsdocode/phobert-finetune-hatespeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tsdocode/phobert-finetune-hatespeech")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tsdocode/phobert-finetune-hatespeech") model = AutoModelForSequenceClassification.from_pretrained("tsdocode/phobert-finetune-hatespeech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b5658805a57816579aeefd30f1628bce817a9755747fc9b34c64133fa79dee31
- Size of remote file:
- 540 MB
- SHA256:
- 41c187a0eda87af0d445c4479b73e9d904a2ba1891e2084e4a1366886fda8b83
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.