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:
- aa214a21d36d23083a8022149046cdda3ec4e461e733dfa8e3dbcecfdba0d4c0
- Size of remote file:
- 3.2 kB
- SHA256:
- 9f0c6d91b436c23c91af6fcd6565ae1ee0e9c2a78db6f5049018df3e3cdc25fb
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