Transformers
PyTorch
Safetensors
Korean
longt5
text2text-generation
Generated from Trainer
Eval Results (legacy)
Instructions to use KETI-AIR-Downstream/long-ke-t5-base-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KETI-AIR-Downstream/long-ke-t5-base-summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KETI-AIR-Downstream/long-ke-t5-base-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("KETI-AIR-Downstream/long-ke-t5-base-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from KETI-AIR-Downstream/long-ke-t5-base-summarization: direct link, hf CLI and curl.
- Browser
- Download file 2.16 kB
-
https://huggingface.co/KETI-AIR-Downstream/long-ke-t5-base-summarization/resolve/50fbb4e963aa8cfd594794708d61f70c07e28931/README.md
- Command line
-
hf download hf://KETI-AIR-Downstream/long-ke-t5-base-summarization@50fbb4e963aa8cfd594794708d61f70c07e28931/README.md
-
curl -L -o README.md https://huggingface.co/KETI-AIR-Downstream/long-ke-t5-base-summarization/resolve/50fbb4e963aa8cfd594794708d61f70c07e28931/README.md
2.16 kB
metadata
tags:
- generated_from_trainer
datasets:
- jsonl_dataset_sum.py
metrics:
- rouge
model-index:
- name: summarization_all
results:
- task:
name: Summarization
type: summarization
dataset:
name: jsonl_dataset_sum.py
type: jsonl_dataset_sum.py
config: 'null'
split: None
metrics:
- name: Rouge1
type: rouge
value: 21.7197
license: artistic-2.0
language:
- ko
summarization_all
This model is a fine-tuned version of KETI-AIR/long-ke-t5-base on the jsonl_dataset_sum.py dataset. It achieves the following results on the evaluation set:
- Loss: 1.0758
- Rouge1: 21.7197
- Rouge2: 10.1392
- Rougel: 21.1499
- Rougelsum: 21.173
- Gen Len: 87.4589
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: 0.001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.2171 | 1.0 | 184670 | 1.2070 | 20.611 | 9.2868 | 20.0833 | 20.1095 | 87.4065 |
| 1.0916 | 2.0 | 369340 | 1.1190 | 21.3264 | 9.8656 | 20.7683 | 20.8005 | 88.0284 |
| 0.9823 | 3.0 | 554010 | 1.0758 | 21.7197 | 10.1392 | 21.1499 | 21.173 | 87.4589 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.0
- Datasets 2.8.0
- Tokenizers 0.13.2