Instructions to use Quexoo/vision-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Quexoo/vision-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Quexoo/vision-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Quexoo/vision-classifier") model = AutoModelForSequenceClassification.from_pretrained("Quexoo/vision-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Quexoo/vision-classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.84 kB
-
https://huggingface.co/Quexoo/vision-classifier/resolve/main/README.md
- Command line
-
hf download hf://Quexoo/vision-classifier/README.md
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curl -L -o README.md https://huggingface.co/Quexoo/vision-classifier/resolve/main/README.md
1.84 kB
metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: vision-classifier
results: []
widget:
- text: 'Age: 40-64 years, Gender: Female, Race: White, non-Hispanic, Diabetes: No'
pipeline_tag: text-classification
vision-classifier
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0002
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 173 | 0.0002 |
| No log | 2.0 | 346 | 0.0002 |
| 0.0003 | 3.0 | 519 | 0.0002 |
| 0.0003 | 4.0 | 692 | 0.0002 |
| 0.0003 | 5.0 | 865 | 0.0002 |
| 0.0003 | 6.0 | 1038 | 0.0002 |
| 0.0003 | 7.0 | 1211 | 0.0002 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu118
- Datasets 2.19.1
- Tokenizers 0.20.3