Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use daveni/aesthetic_attribute_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daveni/aesthetic_attribute_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="daveni/aesthetic_attribute_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("daveni/aesthetic_attribute_classifier") model = AutoModelForSequenceClassification.from_pretrained("daveni/aesthetic_attribute_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a7940973625c4a0802824e5afeb8a80c0a0f26a6acad21266896de16f8d1be9
- Size of remote file:
- 268 MB
- SHA256:
- 6ca98c8bffbde7970ff17903bb39e79b92f3d9731564dc2ab319e21707ab8c4c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.