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# mongolian-gpt2-ner

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: 0.2599
- Precision: 0.1483
- Recall: 0.2561
- F1: 0.1878
- Accuracy: 0.9149

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4822        | 1.0   | 477  | 0.3452          | 0.1156    | 0.2072 | 0.1484 | 0.8876   |
| 0.3376        | 2.0   | 954  | 0.3196          | 0.1369    | 0.2304 | 0.1717 | 0.8975   |
| 0.3084        | 3.0   | 1431 | 0.2915          | 0.1242    | 0.2257 | 0.1603 | 0.9015   |
| 0.2889        | 4.0   | 1908 | 0.2800          | 0.1328    | 0.2375 | 0.1704 | 0.9063   |
| 0.275         | 5.0   | 2385 | 0.2734          | 0.1439    | 0.2452 | 0.1814 | 0.9099   |
| 0.264         | 6.0   | 2862 | 0.2691          | 0.1426    | 0.2420 | 0.1795 | 0.9115   |
| 0.256         | 7.0   | 3339 | 0.2639          | 0.1411    | 0.2442 | 0.1789 | 0.9129   |
| 0.2498        | 8.0   | 3816 | 0.2628          | 0.1482    | 0.2511 | 0.1864 | 0.9135   |
| 0.2438        | 9.0   | 4293 | 0.2603          | 0.1483    | 0.2548 | 0.1875 | 0.9143   |
| 0.2388        | 10.0  | 4770 | 0.2599          | 0.1483    | 0.2561 | 0.1878 | 0.9149   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+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": "mongolian-gpt2-ner", "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 

mongolian-gpt2-ner
==================


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


* Loss: 0.2599
* Precision: 0.1483
* Recall: 0.2561
* F1: 0.1878
* Accuracy: 0.9149


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 4.29.2
* Pytorch 2.0.1+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 4.29.2
* Pytorch 2.0.1+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 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3