Instructions to use amphora/FinABSA-DeBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amphora/FinABSA-DeBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amphora/FinABSA-DeBERTa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amphora/FinABSA-DeBERTa") model = AutoModelForSequenceClassification.from_pretrained("amphora/FinABSA-DeBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from amphora/FinABSA-DeBERTa: direct link, hf CLI and curl.
- Browser
- Download file 737 MB
-
https://huggingface.co/amphora/FinABSA-DeBERTa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://amphora/FinABSA-DeBERTa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/amphora/FinABSA-DeBERTa/resolve/main/pytorch_model.bin
737 MB
- Xet hash:
- 0028f2389e79780b9f4ee54b1b3a94479d3d28877236f086dc179fd04d3d09e6
- Size of remote file:
- 737 MB
- SHA256:
- 329163e1534818c8d17467bfad149839e55d4a9173b0d064c2acb125229dcb0a
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