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# gpt2-kl_1_04_hscnspecial-hs_cn

This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5848

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 21
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 73.7379       | 0.02  | 10   | 69.5698         |
| 46.2931       | 0.04  | 20   | 32.9010         |
| 13.6754       | 0.06  | 30   | 10.6422         |
| 6.462         | 0.08  | 40   | 4.5318          |
| 2.8639        | 0.1   | 50   | 2.0239          |
| 1.5179        | 0.12  | 60   | 1.0382          |
| 1.2111        | 0.14  | 70   | 0.9591          |
| 1.1308        | 0.16  | 80   | 0.7130          |
| 0.965         | 0.18  | 90   | 0.6553          |
| 0.9107        | 0.2   | 100  | 0.6113          |
| 0.8141        | 0.22  | 110  | 0.5902          |
| 0.7915        | 0.24  | 120  | 0.5813          |
| 0.8184        | 0.26  | 130  | 0.5831          |
| 0.7759        | 0.28  | 140  | 0.5860          |
| 0.7813        | 0.3   | 150  | 0.5848          |


### Framework versions

- Transformers 4.28.0
- Pytorch 1.11.0+cu113
- Datasets 2.11.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-kl_1_04_hscnspecial-hs_cn", "results": []}]}
transformers pytorch gpt2 text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

gpt2-kl\_1\_04\_hscnspecial-hs\_cn
==================================


This model is a fine-tuned version of gpt2-medium on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5848


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 21
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3.0


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 1.11.0+cu113
* Datasets 2.11.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 21
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 1.11.0+cu113
* Datasets 2.11.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 21
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 1.11.0+cu113
* Datasets 2.11.0
* Tokenizers 0.12.1