Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use hsshssh/keti-t5-finetuned-summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsshssh/keti-t5-finetuned-summary with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hsshssh/keti-t5-finetuned-summary") model = AutoModelForSeq2SeqLM.from_pretrained("hsshssh/keti-t5-finetuned-summary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from hsshssh/keti-t5-finetuned-summary: direct link, hf CLI and curl.
- Browser
- Download file 1.12 kB
-
https://huggingface.co/hsshssh/keti-t5-finetuned-summary/resolve/main/README.md
- Command line
-
hf download hf://hsshssh/keti-t5-finetuned-summary/README.md
-
curl -L -o README.md https://huggingface.co/hsshssh/keti-t5-finetuned-summary/resolve/main/README.md
1.12 kB
metadata
license: apache-2.0
base_model: hsshssh/keti-t5-finetuned-summary
tags:
- generated_from_trainer
model-index:
- name: keti-t5-finetuned-summary
results: []
keti-t5-finetuned-summary
This model is a fine-tuned version of hsshssh/keti-t5-finetuned-summary on the None 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: 4e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2