Token Classification
Transformers
PyTorch
English
deberta-v2
fine-tune
deberta
absa-model
sentiment-classification
aspect-based-sentiment-classification
sequence-labeling
sentiment-analysis
deberta-finetune
Instructions to use sajida-dev/fine-tune-deberta-v3-base-end2end-absa-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sajida-dev/fine-tune-deberta-v3-base-end2end-absa-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sajida-dev/fine-tune-deberta-v3-base-end2end-absa-model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sajida-dev/fine-tune-deberta-v3-base-end2end-absa-model") model = AutoModelForTokenClassification.from_pretrained("sajida-dev/fine-tune-deberta-v3-base-end2end-absa-model") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1 opened 4 months ago
by
SFconvertbot