Instructions to use anshr/distilbert_reward_model_01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anshr/distilbert_reward_model_01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anshr/distilbert_reward_model_01")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anshr/distilbert_reward_model_01") model = AutoModelForSequenceClassification.from_pretrained("anshr/distilbert_reward_model_01", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 85882e535276f5df68f6438fc6d13c705525bd858fd5b87213a315392b31f76c
- Size of remote file:
- 268 MB
- SHA256:
- 3564b0ed90d10d7766d211ec1642b5f6e4507ae099fc02790c113065f3ce1878
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