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:
- ec3a67f9c845733326b67587ce9523983936604e9a694aedf233d17d6e499670
- Size of remote file:
- 4.54 kB
- SHA256:
- 8024fe298f0b213b457bfe4a9a6c4bc5a8241e9e79c58b11270f2eb7bba1de7d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.