Text Classification
Transformers
PyTorch
Safetensors
deberta-v2
sentiment-analysis
aspect-based-sentiment-analysis
deberta
pyabsa
efficient
lightweight
production-ready
no-llm
Instructions to use yangheng/deberta-v3-large-absa-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yangheng/deberta-v3-large-absa-v1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yangheng/deberta-v3-large-absa-v1.1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yangheng/deberta-v3-large-absa-v1.1") model = AutoModelForSequenceClassification.from_pretrained("yangheng/deberta-v3-large-absa-v1.1") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 347bba03514d36add7da91695606c82c6e7a309da0af30152a77779f5bef9277
- Size of remote file:
- 1.74 GB
- SHA256:
- ffd4285f16e214acb5aa397bf46aa5f37aea67e6c1fa8a09f9f7c88d5777d921
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