Instructions to use natriistorm/DeepPavlov-ABSA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use natriistorm/DeepPavlov-ABSA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="natriistorm/DeepPavlov-ABSA")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("natriistorm/DeepPavlov-ABSA") model = AutoModelForTokenClassification.from_pretrained("natriistorm/DeepPavlov-ABSA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5db90946db719b277cdb4813b8f621252a2d7583b61ec0d42fc7a44da562559b
- Size of remote file:
- 1.11 GB
- SHA256:
- 448b1be2ce34357148d1b3a0c718a49fbb217f72b3cb8cb3c9eb488131042801
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