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# gpt-2-10000

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: 0.2551
- Precision: 0.1523
- Recall: 0.2608
- F1: 0.1923
- Accuracy: 0.9175

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4502        | 1.0   | 477  | 0.3178          | 0.1351    | 0.2289 | 0.1699 | 0.8953   |
| 0.3283        | 2.0   | 954  | 0.3014          | 0.1227    | 0.2220 | 0.1581 | 0.8985   |
| 0.3016        | 3.0   | 1431 | 0.2768          | 0.1441    | 0.2379 | 0.1795 | 0.9077   |
| 0.2824        | 4.0   | 1908 | 0.2687          | 0.1442    | 0.2415 | 0.1806 | 0.9103   |
| 0.2686        | 5.0   | 2385 | 0.2697          | 0.1374    | 0.2383 | 0.1743 | 0.9086   |
| 0.2568        | 6.0   | 2862 | 0.2573          | 0.1450    | 0.2525 | 0.1842 | 0.9140   |
| 0.2472        | 7.0   | 3339 | 0.2534          | 0.1492    | 0.2574 | 0.1889 | 0.9166   |
| 0.2405        | 8.0   | 3816 | 0.2548          | 0.1413    | 0.2515 | 0.1809 | 0.9153   |
| 0.2345        | 9.0   | 4293 | 0.2545          | 0.1489    | 0.2564 | 0.1884 | 0.9163   |
| 0.2299        | 10.0  | 4770 | 0.2551          | 0.1523    | 0.2608 | 0.1923 | 0.9175   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["mn"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "gpt-2-10000", "results": []}]}
transformers pytorch tensorboard gpt2 token-classification generated_from_trainer mn license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #gpt2 #token-classification #generated_from_trainer #mn #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

gpt-2-10000
===========


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


* Loss: 0.2551
* Precision: 0.1523
* Recall: 0.2608
* F1: 0.1923
* Accuracy: 0.9175


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: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #gpt2 #token-classification #generated_from_trainer #mn #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3