Instructions to use Waynehillsdev/Waynehills_summary_tensorflow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Waynehillsdev/Waynehills_summary_tensorflow with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Waynehillsdev/Waynehills_summary_tensorflow") model = AutoModelForSeq2SeqLM.from_pretrained("Waynehillsdev/Waynehills_summary_tensorflow", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Waynehillsdev/Waynehills_summary_tensorflow: direct link, hf CLI and curl.
- Browser
- Download file 967 Bytes
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https://huggingface.co/Waynehillsdev/Waynehills_summary_tensorflow/resolve/main/README.md
- Command line
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hf download hf://Waynehillsdev/Waynehills_summary_tensorflow/README.md
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curl -L -o README.md https://huggingface.co/Waynehillsdev/Waynehills_summary_tensorflow/resolve/main/README.md
967 Bytes
metadata
tags:
- generated_from_keras_callback
model-index:
- name: Waynehills_summary_tensorflow
results: []
Waynehills_summary_tensorflow
This model is a fine-tuned version of KETI-AIR/ke-t5-base-ko on an unknown dataset. It achieves the following results on the evaluation set:
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:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.15.0
- TensorFlow 2.7.0
- Datasets 1.17.0
- Tokenizers 0.10.3