Text Classification
Transformers
Safetensors
Japanese
modernbert
custom_code
text-embeddings-inference
Instructions to use Aratako/Japanese-Novel-Reward-310m-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aratako/Japanese-Novel-Reward-310m-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aratako/Japanese-Novel-Reward-310m-v2", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aratako/Japanese-Novel-Reward-310m-v2", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("Aratako/Japanese-Novel-Reward-310m-v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 4de1ffd789ec9e16078acce4da754f13b04970e029dd09da0e61a001d8ba0176
- Size of remote file:
- 472 kB
- SHA256:
- c3991fa0fc99ffd3a2033eb59503220082bd4c71930fdd117cc3ea50dbf9f19e
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