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# summarization_all

This model is a fine-tuned version of [KETI-AIR/long-ke-t5-base](https://huggingface.co/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
{"language": ["ko"], "license": "artistic-2.0", "tags": ["generated_from_trainer"], "datasets": ["jsonl_dataset_sum.py"], "metrics": ["rouge"], "widget": [{"text": "summarization-num_lines-1: \ud604\ub300\uc790\ub3d9\ucc28\ub294 18\uc77c(\ud604\uc9c0 \uc2dc\uac04) \uc774\ud0c8\ub9ac\uc544 \ub808\uc774\ud06c \ucf54\ubaa8\uc5d0\uc11c \uac1c\ucd5c\ub41c '\ud604\ub300 \ub9ac\uc720\ub2c8\uc628' \ud589\uc0ac\uc5d0\uc11c '\ud3ec\ub2c8 \ucfe0\ud398 \ucf58\uc149\ud2b8' \ubcf5\uc6d0 \ubaa8\ub378\uc744 \uc138\uacc4\uc5d0 \uccab \uacf5\uac1c\ud588\uc2b5\ub2c8\ub2e4. \uc774 \ud504\ub85c\uc81d\ud2b8\ub294 \ud604\ub300\ucc28\uc758 \ucc3d\uc5c5\uc790\uc778 \uc815\uc8fc\uc601 \uc120\ub300 \ud68c\uc7a5\uc758 \uc218\ucd9c\ubcf4\uad6d(\u8f38\u51fa\u5831\u570b) \uc815\uc2e0\uacfc \ud3ec\ub2c8 \ucfe0\ud398\ub97c \ud1b5\ud55c \uae00\ub85c\ubc8c \ube0c\ub79c\ub4dc \uc815\ub9bd\uc5d0 \ub300\ud55c \ub04a\uc784\uc5c6\ub294 \uc5f4\uc815\uacfc \ub3c4\uc804 \uc815\uc2e0\uc744 \uc7ac\uc870\uba85\ud558\uae30 \uc704\ud55c \uac83\uc785\ub2c8\ub2e4. \ud604\ub300\ucc28\uc5d0 \ub530\ub974\uba74, \uc774\ubc88 \ud604\ub300 \ub9ac\uc720\ub2c8\uc628 \ud589\uc0ac\ub294 \ud68c\uc0ac\uc758 \uc5ed\uc0ac\ub97c \ub2e4\uc2dc \ub3cc\uc544\ubcf4\uba70 \ubcc0\ud558\uc9c0 \uc54a\ub294 \ubbf8\ub798 \uc9c0\ud5a5\uc801\uc778 \ube44\uc804\uacfc \ubc29\ud5a5\uc131\uc744 \uacf5\uc720\ud558\ub294 \ube0c\ub79c\ub4dc \uc720\uc0b0 \ud589\uc0ac\uc785\ub2c8\ub2e4.", "example_title": "sample 1"}], "base_model": "KETI-AIR/long-ke-t5-base", "model-index": [{"name": "summarization_all", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "jsonl_dataset_sum.py", "type": "jsonl_dataset_sum.py", "config": "null", "split": "None"}, "metrics": [{"type": "rouge", "value": 21.7197, "name": "Rouge1"}]}]}]}
transformers pytorch safetensors longt5 text2text-generation generated_from_trainer ko dataset:jsonl_dataset_sum.py base_model:KETI-AIR/long-ke-t5-base license:artistic-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #longt5 #text2text-generation #generated_from_trainer #ko #dataset-jsonl_dataset_sum.py #base_model-KETI-AIR/long-ke-t5-base #license-artistic-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

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



### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.0
* Datasets 2.8.0
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.0
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #safetensors #longt5 #text2text-generation #generated_from_trainer #ko #dataset-jsonl_dataset_sum.py #base_model-KETI-AIR/long-ke-t5-base #license-artistic-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.0
* Datasets 2.8.0
* Tokenizers 0.13.2