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# roberta-el-ner18

This model is a fine-tuned version of [cvcio/roberta-el-news](https://huggingface.co/cvcio/roberta-el-news) on the [elNER](https://github.com/nmpartzio/elNER) dataset.
It achieves the following results on the evaluation set:

- Loss: 0.1380
- Precision: 0.9138
- Recall: 0.9289
- F1: 0.9213
- Accuracy: 0.9832

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

More information needed

### Training hyperparameters

The following hyperparameters were used during training:

- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 60.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4245        | 1.87  | 250  | 0.1622          | 0.7727    | 0.8096 | 0.7907 | 0.9597   |
| 0.0798        | 3.73  | 500  | 0.0841          | 0.8587    | 0.9005 | 0.8791 | 0.9776   |
| 0.0487        | 5.6   | 750  | 0.0812          | 0.8850    | 0.9140 | 0.8992 | 0.9806   |
| 0.0222        | 7.46  | 1000 | 0.0855          | 0.9001    | 0.9180 | 0.9089 | 0.9819   |
| 0.0141        | 9.33  | 1250 | 0.0903          | 0.9023    | 0.9230 | 0.9125 | 0.9827   |
| 0.0079        | 11.19 | 1500 | 0.1006          | 0.9067    | 0.9258 | 0.9161 | 0.9823   |
| 0.0063        | 13.06 | 1750 | 0.1020          | 0.9049    | 0.9296 | 0.9171 | 0.9826   |
| 0.0039        | 14.93 | 2000 | 0.1097          | 0.9078    | 0.9246 | 0.9161 | 0.9820   |
| 0.004         | 16.79 | 2250 | 0.1119          | 0.9084    | 0.9239 | 0.9161 | 0.9825   |
| 0.0024        | 18.66 | 2500 | 0.1166          | 0.9086    | 0.9268 | 0.9177 | 0.9828   |
| 0.0029        | 20.52 | 2750 | 0.1192          | 0.9106    | 0.9260 | 0.9182 | 0.9825   |
| 0.0023        | 22.39 | 3000 | 0.1161          | 0.9085    | 0.9284 | 0.9183 | 0.9829   |
| 0.0022        | 24.25 | 3250 | 0.1238          | 0.9078    | 0.9281 | 0.9178 | 0.9825   |
| 0.0021        | 26.12 | 3500 | 0.1232          | 0.9082    | 0.9239 | 0.9160 | 0.9821   |
| 0.0013        | 27.99 | 3750 | 0.1253          | 0.9050    | 0.9296 | 0.9172 | 0.9824   |
| 0.0012        | 29.85 | 4000 | 0.1247          | 0.9075    | 0.9284 | 0.9179 | 0.9827   |
| 0.0014        | 31.72 | 4250 | 0.1263          | 0.9063    | 0.9237 | 0.9149 | 0.9823   |
| 0.0012        | 33.58 | 4500 | 0.1295          | 0.9028    | 0.9272 | 0.9148 | 0.9827   |
| 0.001         | 35.45 | 4750 | 0.1341          | 0.9107    | 0.9305 | 0.9205 | 0.9831   |
| 0.001         | 37.31 | 5000 | 0.1296          | 0.9122    | 0.9298 | 0.9209 | 0.9833   |
| 0.0013        | 39.18 | 5250 | 0.1273          | 0.9058    | 0.9249 | 0.9153 | 0.9823   |
| 0.0007        | 41.04 | 5500 | 0.1296          | 0.9053    | 0.9261 | 0.9156 | 0.9824   |
| 0.0007        | 42.91 | 5750 | 0.1326          | 0.9083    | 0.9303 | 0.9192 | 0.9830   |
| 0.0006        | 44.78 | 6000 | 0.1328          | 0.9088    | 0.9270 | 0.9178 | 0.9828   |
| 0.0006        | 46.64 | 6250 | 0.1362          | 0.9103    | 0.9314 | 0.9207 | 0.9831   |
| 0.0004        | 48.51 | 6500 | 0.1351          | 0.9132    | 0.9288 | 0.9209 | 0.9830   |
| 0.0005        | 50.37 | 6750 | 0.1325          | 0.9138    | 0.9270 | 0.9204 | 0.9830   |
| 0.0005        | 52.24 | 7000 | 0.1330          | 0.9115    | 0.9272 | 0.9193 | 0.9832   |
| 0.0005        | 54.1  | 7250 | 0.1356          | 0.9119    | 0.9270 | 0.9194 | 0.9833   |
| 0.0004        | 55.97 | 7500 | 0.1367          | 0.9132    | 0.9274 | 0.9202 | 0.9832   |
| 0.0003        | 57.84 | 7750 | 0.1380          | 0.9141    | 0.9288 | 0.9214 | 0.9832   |
| 0.0004        | 59.7  | 8000 | 0.1380          | 0.9138    | 0.9289 | 0.9213 | 0.9832   |

### Eval results

|      | Precision | Recall | F1     | Accuracy |
|:----:|:---------:|:------:|:------:|:--------:|
| eval | 0.9138    | 0.9289 | 0.9213 | 0.9832   |
| test | 0.9097    | 0.9232 | 0.9164 | 0.9808   |

### Framework versions

- Transformers 4.29.2
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

## Authors

Dimitris Papaevagelou - [@andefined](https://huggingface.co/andefined)

## About Us

[Civic Information Office](https://cvcio.org/) is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest.
{"language": "el", "license": "gpl-3.0", "tags": ["generated_from_trainer", "roberta", "Greek", "ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-el-ner4", "results": []}]}
transformers safetensors roberta token-classification generated_from_trainer Greek ner el license:gpl-3.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #safetensors #roberta #token-classification #generated_from_trainer #Greek #ner #el #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us 

roberta-el-ner18
================


This model is a fine-tuned version of cvcio/roberta-el-news on the elNER dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1380
* Precision: 0.9138
* Recall: 0.9289
* F1: 0.9213
* Accuracy: 0.9832


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


More information needed


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 60.0


### Training results



### Eval results



### Framework versions


* Transformers 4.29.2
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2


Authors
-------


Dimitris Papaevagelou - @andefined


About Us
--------


Civic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest.



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 60.0 ### Training results ### Eval results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2


Authors
-------


Dimitris Papaevagelou - @andefined


About Us
--------


Civic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest.
TAGS
#transformers #safetensors #roberta #token-classification #generated_from_trainer #Greek #ner #el #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 60.0 ### Training results ### Eval results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2


Authors
-------


Dimitris Papaevagelou - @andefined


About Us
--------


Civic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest.