Instructions to use mgreenbe/bertlet-base-uncased-for-sequence-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgreenbe/bertlet-base-uncased-for-sequence-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mgreenbe/bertlet-base-uncased-for-sequence-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mgreenbe/bertlet-base-uncased-for-sequence-classification") model = AutoModelForSequenceClassification.from_pretrained("mgreenbe/bertlet-base-uncased-for-sequence-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb1d818f3a35c3f9f77785f61a3a439e20bbaec6508498d590d2dffbe92e8149
- Size of remote file:
- 97.7 MB
- SHA256:
- af8b8de0277b7d39e85999f9753cf869c9b975d180bc79e98651407aed601c57
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