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
Librarian Bot: Add base_model information to model
#2
by librarian-bot - opened
README.md
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- recall
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- accuracy
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model-index:
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- name: aesthetic_attribute_classifier
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results: []
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widget:
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- text: Check your vertical on the main support; it looks a little off. I'd also like to see how it looks with a bit of the sky cropped from the photo
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- recall
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- f1
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- accuracy
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widget:
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- text: Check your vertical on the main support; it looks a little off. I'd also like
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to see how it looks with a bit of the sky cropped from the photo
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base_model: distilbert-base-uncased
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model-index:
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- name: aesthetic_attribute_classifier
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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