Instructions to use stagvn/vi-fin-relevant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stagvn/vi-fin-relevant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="stagvn/vi-fin-relevant")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("stagvn/vi-fin-relevant") model = AutoModelForSequenceClassification.from_pretrained("stagvn/vi-fin-relevant", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8308e6f8b2aa3dfaf0b45c94616259a62b586c53e8cb3a18ad85993f8924f562
- Size of remote file:
- 461 MB
- SHA256:
- bda01f8d57e97b7b3f1fbaeff86b173444e9f1741a4c5434b3b07b1c8235906b
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