Instructions to use Transformers123/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Transformers123/output with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("Transformers123/output") model = SegformerForSemanticSegmentation.from_pretrained("Transformers123/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Transformers123/output: direct link, hf CLI and curl.
- Browser
- Download file 997 Bytes
-
https://huggingface.co/Transformers123/output/resolve/main/README.md
- Command line
-
hf download hf://Transformers123/output/README.md
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curl -L -o README.md https://huggingface.co/Transformers123/output/resolve/main/README.md
997 Bytes
metadata
license: other
base_model: nvidia/mit-b2
tags:
- generated_from_trainer
model-index:
- name: output
results: []
output
This model is a fine-tuned version of nvidia/mit-b2 on an unknown 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1