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# gpt2_small_summarized

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6935
- Accuracy: 0.79
- Precision: 0.2632
- Recall: 0.1515
- F1: 0.1923
- D-index: 1.4571

## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1600
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | D-index |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log        | 1.0   | 200  | 0.9451          | 0.77     | 0.0667    | 0.0303 | 0.0417 | 1.3827  |
| No log        | 2.0   | 400  | 0.7625          | 0.81     | 0.0       | 0.0    | 0.0    | 1.4265  |
| 2.1174        | 3.0   | 600  | 0.7145          | 0.835    | 0.0       | 0.0    | 0.0    | 1.4607  |
| 2.1174        | 4.0   | 800  | 1.0087          | 0.835    | 0.0       | 0.0    | 0.0    | 1.4607  |
| 0.6744        | 5.0   | 1000 | 0.6728          | 0.825    | 0.0       | 0.0    | 0.0    | 1.4471  |
| 0.6744        | 6.0   | 1200 | 0.7295          | 0.725    | 0.1944    | 0.2121 | 0.2029 | 1.3899  |
| 0.6744        | 7.0   | 1400 | 2.0423          | 0.825    | 0.3333    | 0.0606 | 0.1026 | 1.4704  |
| 0.3582        | 8.0   | 1600 | 2.5923          | 0.685    | 0.1591    | 0.2121 | 0.1818 | 1.3332  |
| 0.3582        | 9.0   | 1800 | 2.9312          | 0.605    | 0.1974    | 0.4545 | 0.2752 | 1.3098  |
| 0.1089        | 10.0  | 2000 | 3.0778          | 0.81     | 0.0       | 0.0    | 0.0    | 1.4265  |
| 0.1089        | 11.0  | 2200 | 3.0158          | 0.785    | 0.25      | 0.1515 | 0.1887 | 1.4503  |
| 0.1089        | 12.0  | 2400 | 3.0195          | 0.8      | 0.3333    | 0.2121 | 0.2593 | 1.4934  |
| 0.0376        | 13.0  | 2600 | 3.3198          | 0.77     | 0.2593    | 0.2121 | 0.2333 | 1.4525  |
| 0.0376        | 14.0  | 2800 | 3.4092          | 0.77     | 0.2593    | 0.2121 | 0.2333 | 1.4525  |
| 0.0012        | 15.0  | 3000 | 3.5722          | 0.76     | 0.2       | 0.1515 | 0.1724 | 1.4157  |
| 0.0012        | 16.0  | 3200 | 3.5919          | 0.775    | 0.25      | 0.1818 | 0.2105 | 1.4480  |
| 0.0012        | 17.0  | 3400 | 3.5835          | 0.795    | 0.2778    | 0.1515 | 0.1961 | 1.4639  |
| 0.0045        | 18.0  | 3600 | 3.6829          | 0.785    | 0.25      | 0.1515 | 0.1887 | 1.4503  |
| 0.0045        | 19.0  | 3800 | 3.6905          | 0.785    | 0.25      | 0.1515 | 0.1887 | 1.4503  |
| 0.0008        | 20.0  | 4000 | 3.6935          | 0.79     | 0.2632    | 0.1515 | 0.1923 | 1.4571  |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "gpt2_small_summarized", "results": []}]}
transformers pytorch tensorboard gpt2 text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

gpt2\_small\_summarized
=======================


This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 3.6935
* Accuracy: 0.79
* Precision: 0.2632
* Recall: 0.1515
* F1: 0.1923
* D-index: 1.4571


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: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1600
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1600
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-classification #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: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1600
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3