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# gpt2_sm_cv_defined_4

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: 2.4859
- Accuracy: 0.777
- Precision: 0.3889
- Recall: 0.2513
- F1: 0.3053
- D-index: 1.4874

## 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  | 1.2496          | 0.469    | 0.1830    | 0.4974 | 0.2676 | 1.1202  |
| 2.4045        | 2.0   | 500  | 0.6575          | 0.679    | 0.2056    | 0.2256 | 0.2152 | 1.3395  |
| 2.4045        | 3.0   | 750  | 0.5358          | 0.784    | 0.2439    | 0.0513 | 0.0847 | 1.4262  |
| 0.5054        | 4.0   | 1000 | 0.5534          | 0.786    | 0.3333    | 0.0974 | 0.1508 | 1.4457  |
| 0.5054        | 5.0   | 1250 | 0.5221          | 0.785    | 0.2727    | 0.0615 | 0.1004 | 1.4313  |
| 0.4682        | 6.0   | 1500 | 0.5396          | 0.765    | 0.3276    | 0.1949 | 0.2444 | 1.4510  |
| 0.4682        | 7.0   | 1750 | 0.5442          | 0.766    | 0.3415    | 0.2154 | 0.2642 | 1.4596  |
| 0.4097        | 8.0   | 2000 | 0.7828          | 0.79     | 0.2581    | 0.0410 | 0.0708 | 1.4309  |
| 0.4097        | 9.0   | 2250 | 0.6443          | 0.771    | 0.3607    | 0.2256 | 0.2776 | 1.4701  |
| 0.3341        | 10.0  | 2500 | 0.6839          | 0.76     | 0.3529    | 0.2769 | 0.3103 | 1.4727  |
| 0.3341        | 11.0  | 2750 | 0.7968          | 0.725    | 0.3095    | 0.3333 | 0.3210 | 1.4434  |
| 0.2456        | 12.0  | 3000 | 1.0615          | 0.771    | 0.3534    | 0.2103 | 0.2637 | 1.4648  |
| 0.2456        | 13.0  | 3250 | 1.7036          | 0.786    | 0.3797    | 0.1538 | 0.2190 | 1.4657  |
| 0.1537        | 14.0  | 3500 | 1.5848          | 0.741    | 0.3280    | 0.3128 | 0.3202 | 1.4587  |
| 0.1537        | 15.0  | 3750 | 1.5904          | 0.727    | 0.3125    | 0.3333 | 0.3226 | 1.4462  |
| 0.1323        | 16.0  | 4000 | 1.9229          | 0.685    | 0.3       | 0.4615 | 0.3636 | 1.4311  |
| 0.1323        | 17.0  | 4250 | 2.2383          | 0.785    | 0.3684    | 0.1436 | 0.2066 | 1.4607  |
| 0.1117        | 18.0  | 4500 | 2.4084          | 0.799    | 0.4167    | 0.0769 | 0.1299 | 1.4564  |
| 0.1117        | 19.0  | 4750 | 2.5225          | 0.798    | 0.4426    | 0.1385 | 0.2109 | 1.4769  |
| 0.0759        | 20.0  | 5000 | 2.4859          | 0.777    | 0.3889    | 0.2513 | 0.3053 | 1.4874  |


### 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_cv_defined_4", "results": []}]}
transformers pytorch gpt2 text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

gpt2\_sm\_cv\_defined\_4
========================


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


* Loss: 2.4859
* Accuracy: 0.777
* Precision: 0.3889
* Recall: 0.2513
* F1: 0.3053
* D-index: 1.4874


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 #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