Instructions to use Dilwolf/Product_Review with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dilwolf/Product_Review with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dilwolf/Product_Review")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Dilwolf/Product_Review") model = AutoModelForSequenceClassification.from_pretrained("Dilwolf/Product_Review", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae3277e9c40c6202581f6d727387d8cbc29e61fe4e99329111a9d3770c555b28
- Size of remote file:
- 5.05 kB
- SHA256:
- 224cb6a4dfcb6064255846f04599871b4e2d3f42fa194b2d19371d43edc12966
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