Text Classification
Transformers
PyTorch
English
longformer
macroeconomics
automated summary evaluation
content
Instructions to use wesleymorris/longformer-content-global with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wesleymorris/longformer-content-global with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wesleymorris/longformer-content-global")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wesleymorris/longformer-content-global") model = AutoModelForSequenceClassification.from_pretrained("wesleymorris/longformer-content-global", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34c0897ec96805aca165a242bb2e6f6d581195cc9c7090a35b7320fce1cdca5f
- Size of remote file:
- 595 MB
- SHA256:
- ff0e6ea314bc18332f6439e51d8b6345d6a20581f46a8ceddf57b885c2f444b0
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