Instructions to use addykan/model_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use addykan/model_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="addykan/model_data")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("addykan/model_data") model = AutoModelForSequenceClassification.from_pretrained("addykan/model_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf6dac88433b03af1ceef91444a13a23824221efaf9f06bdb6ae49135cbe4b03
- Size of remote file:
- 4.54 kB
- SHA256:
- 86b1f2cd43a7b3319ec1b495a56271d839273bd7b5ee54fd118174638db29f46
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