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# gpt2_sm_gen1

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: 2.8305
- Accuracy: 0.798
- Precision: 0.6346
- Recall: 0.1528
- F1: 0.2463
- D-index: 1.4919

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 8000
- 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   | 250  | 1.2636          | 0.706    | 0.25      | 0.1806 | 0.2097 | 1.3706  |
| 1.4845        | 2.0   | 500  | 0.7018          | 0.734    | 0.25      | 0.1157 | 0.1582 | 1.3887  |
| 1.4845        | 3.0   | 750  | 0.6251          | 0.784    | 0.5       | 0.0093 | 0.0182 | 1.4233  |
| 0.5185        | 4.0   | 1000 | 0.5824          | 0.786    | 0.6       | 0.0278 | 0.0531 | 1.4326  |
| 0.5185        | 5.0   | 1250 | 0.5811          | 0.789    | 0.6667    | 0.0463 | 0.0866 | 1.4432  |
| 0.4299        | 6.0   | 1500 | 0.6123          | 0.793    | 0.8       | 0.0556 | 0.1039 | 1.4520  |
| 0.4299        | 7.0   | 1750 | 0.5759          | 0.784    | 0.5       | 0.1389 | 0.2174 | 1.4677  |
| 0.3603        | 8.0   | 2000 | 0.7418          | 0.79     | 0.6154    | 0.0741 | 0.1322 | 1.4541  |
| 0.3603        | 9.0   | 2250 | 0.6740          | 0.766    | 0.4392    | 0.3009 | 0.3571 | 1.4963  |
| 0.2662        | 10.0  | 2500 | 0.8520          | 0.789    | 0.5385    | 0.1620 | 0.2491 | 1.4824  |
| 0.2662        | 11.0  | 2750 | 1.4823          | 0.79     | 0.6154    | 0.0741 | 0.1322 | 1.4541  |
| 0.1838        | 12.0  | 3000 | 1.2440          | 0.789    | 0.5203    | 0.2963 | 0.3776 | 1.5267  |
| 0.1838        | 13.0  | 3250 | 1.3491          | 0.781    | 0.4854    | 0.2315 | 0.3135 | 1.4944  |
| 0.1362        | 14.0  | 3500 | 1.6458          | 0.786    | 0.5093    | 0.2546 | 0.3395 | 1.5089  |
| 0.1362        | 15.0  | 3750 | 1.7559          | 0.794    | 0.5532    | 0.2407 | 0.3355 | 1.5155  |
| 0.1176        | 16.0  | 4000 | 2.3472          | 0.801    | 0.7073    | 0.1343 | 0.2257 | 1.4899  |
| 0.1176        | 17.0  | 4250 | 2.1587          | 0.793    | 0.5849    | 0.1435 | 0.2305 | 1.4818  |
| 0.0812        | 18.0  | 4500 | 2.1713          | 0.793    | 0.5584    | 0.1991 | 0.2935 | 1.5003  |
| 0.0812        | 19.0  | 4750 | 2.1631          | 0.793    | 0.5413    | 0.2731 | 0.3631 | 1.5247  |
| 0.065         | 20.0  | 5000 | 2.8305          | 0.798    | 0.6346    | 0.1528 | 0.2463 | 1.4919  |


### 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_sm_gen1", "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\_sm\_gen1
==============


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


* Loss: 2.8305
* Accuracy: 0.798
* Precision: 0.6346
* Recall: 0.1528
* F1: 0.2463
* D-index: 1.4919


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 8000
* 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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 8000
* 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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 8000
* 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