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# gpt2-multi-news

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6300

## 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: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7978        | 1.0   | 1250 | 2.6300          |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["summarization", "generated_from_trainer"], "datasets": ["multi_news"], "base_model": "gpt2", "model-index": [{"name": "gpt2-multi-news", "results": []}]}
transformers pytorch safetensors gpt2 text-generation summarization generated_from_trainer dataset:multi_news base_model:gpt2 license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #summarization #generated_from_trainer #dataset-multi_news #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

gpt2-multi-news
===============


This model is a fine-tuned version of gpt2 on the multi\_news dataset.
It achieves the following results on the evaluation set:


* Loss: 2.6300


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: 5.6e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #summarization #generated_from_trainer #dataset-multi_news #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3