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:
- 3dd71c37e256f57729243da280f65f0ce466079f48b6840c8bdb870a336ccd5f
- Size of remote file:
- 5 GB
- SHA256:
- 7b48fb058909341cd53c154fffd69c8bd2e01759a6d3684a6ad65479a2cec802
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