Transformers
TensorBoard
Safetensors
vit_mae
pretraining
masked-auto-encoding
Generated from Trainer
Instructions to use crncskn/prtrnlng2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crncskn/prtrnlng2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForPreTraining processor = AutoImageProcessor.from_pretrained("crncskn/prtrnlng2") model = AutoModelForPreTraining.from_pretrained("crncskn/prtrnlng2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from crncskn/prtrnlng2: direct link, hf CLI and curl.
- Browser
- Download file 1 kB
-
https://huggingface.co/crncskn/prtrnlng2/resolve/8e40efbb6b9bb0532ebd01e0a2aa7e40ce0d71d2/README.md
- Command line
-
hf download hf://crncskn/prtrnlng2@8e40efbb6b9bb0532ebd01e0a2aa7e40ce0d71d2/README.md
-
curl -L -o README.md https://huggingface.co/crncskn/prtrnlng2/resolve/8e40efbb6b9bb0532ebd01e0a2aa7e40ce0d71d2/README.md
1 kB
metadata
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: prtrnlng2
results: []
prtrnlng2
This model is a fine-tuned version of on the imagefolder dataset.
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: 3.125e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50.0
Training results
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2