Instructions to use rmtariq/overall-rating-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rmtariq/overall-rating-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rmtariq/overall-rating-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rmtariq/overall-rating-bert") model = AutoModelForSequenceClassification.from_pretrained("rmtariq/overall-rating-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dc13800fb7f2302fea49b210e76a6428e9599d2d1b1b0373272c4d39e320d2e7
- Size of remote file:
- 5.3 kB
- SHA256:
- e708ca2d6c3272d84324ac5eff34fc8fcd459ec17694efc12ed262acef2fb074
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