Instructions to use Fsoft-AIC/dopamin-python-parameters with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fsoft-AIC/dopamin-python-parameters with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fsoft-AIC/dopamin-python-parameters")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fsoft-AIC/dopamin-python-parameters") model = AutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/dopamin-python-parameters") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 584515ae30016811319d94952d90e47612ca38562ead877cce239636fbf80fee
- Size of remote file:
- 612 MB
- SHA256:
- 0250a755b66f0e62d6f5e523d987689b8cb7f9d1e70ef16a78bc7f1fc7daaaf0
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