Instructions to use valurank/distilroberta-bias with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use valurank/distilroberta-bias with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="valurank/distilroberta-bias")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("valurank/distilroberta-bias") model = AutoModelForSequenceClassification.from_pretrained("valurank/distilroberta-bias", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a9ea1cb103545df8bb5ceb560cb1656eeef30def6c97d0e9f856e9f995f0457
- Size of remote file:
- 329 MB
- SHA256:
- e2520b151eb20b28c506cd58d77d6da6e3ed022daee63f39b1df68a51aab20cc
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