Text Classification
Transformers
Safetensors
longformer
reward-model
reranking
literary-style
faithfulness
Instructions to use 3rd-Degree-Burn/LongformerRM-Unison with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 3rd-Degree-Burn/LongformerRM-Unison with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="3rd-Degree-Burn/LongformerRM-Unison")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("3rd-Degree-Burn/LongformerRM-Unison") model = AutoModelForSequenceClassification.from_pretrained("3rd-Degree-Burn/LongformerRM-Unison") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2b322d458f977c61f6360717c6b642f2553e64064c2551e8b233026fe7fbebe
- Size of remote file:
- 5.78 kB
- SHA256:
- 832cbd1d8fc4636ee25d0e16ea97e5fc6c54c12068318296ad9d45bd89998043
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.