Text Classification
Transformers
Safetensors
English
llama
text-generation
Writing
Acdamic_Writing
Scholarly_Writing
Overleaf
LaTex
Natural_Language_Processing
text-embeddings-inference
Instructions to use minnesotanlp/scholawrite-llama3.1-8b-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minnesotanlp/scholawrite-llama3.1-8b-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="minnesotanlp/scholawrite-llama3.1-8b-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("minnesotanlp/scholawrite-llama3.1-8b-classifier") model = AutoModelForMultimodalLM.from_pretrained("minnesotanlp/scholawrite-llama3.1-8b-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 64dca59b54bb1749138e82fee1ab9195c74db2185d49274d38c69900ce3ec348
- Size of remote file:
- 4.89 GB
- SHA256:
- b8d837c0c4394a972ec141bdcf8caa5846e9984d63e915e665378f16e0c96dd0
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