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# roberta-el-ner4

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: 0.0564
- Precision: 0.9116
- Recall: 0.9218
- F1: 0.9167
- Accuracy: 0.9883

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1723        | 1.87  | 250  | 0.0716          | 0.7937    | 0.8657 | 0.8281 | 0.9786   |
| 0.0372        | 3.73  | 500  | 0.0497          | 0.8587    | 0.9215 | 0.8890 | 0.9852   |
| 0.0243        | 5.6   | 750  | 0.0524          | 0.8746    | 0.9263 | 0.8997 | 0.9867   |
| 0.0114        | 7.46  | 1000 | 0.0564          | 0.9116    | 0.9218 | 0.9167 | 0.9883   |
| 0.0071        | 9.33  | 1250 | 0.0602          | 0.9019    | 0.9309 | 0.9161 | 0.9881   |
| 0.0037        | 11.19 | 1500 | 0.0667          | 0.9074    | 0.9306 | 0.9188 | 0.9885   |
| 0.003         | 13.06 | 1750 | 0.0688          | 0.9001    | 0.9306 | 0.9151 | 0.9883   |
| 0.0019        | 14.93 | 2000 | 0.0724          | 0.9082    | 0.9229 | 0.9155 | 0.9883   |
| 0.0022        | 16.79 | 2250 | 0.0745          | 0.9159    | 0.9169 | 0.9164 | 0.9878   |
| 0.0016        | 18.66 | 2500 | 0.0727          | 0.9068    | 0.9249 | 0.9158 | 0.9880   |
| 0.0018        | 20.52 | 2750 | 0.0732          | 0.9088    | 0.9272 | 0.9179 | 0.9887   |
| 0.0014        | 22.39 | 3000 | 0.0767          | 0.9017    | 0.9243 | 0.9129 | 0.9876   |
| 0.0012        | 24.25 | 3250 | 0.0745          | 0.9072    | 0.9206 | 0.9139 | 0.9882   |
| 0.0011        | 26.12 | 3500 | 0.0790          | 0.8995    | 0.9297 | 0.9144 | 0.9878   |
| 0.0008        | 27.99 | 3750 | 0.0786          | 0.9081    | 0.9275 | 0.9177 | 0.9883   |
| 0.0011        | 29.85 | 4000 | 0.0775          | 0.9091    | 0.9277 | 0.9183 | 0.9885   |
| 0.0011        | 31.72 | 4250 | 0.0851          | 0.9005    | 0.9269 | 0.9135 | 0.9879   |
| 0.0007        | 33.58 | 4500 | 0.0848          | 0.9041    | 0.9223 | 0.9131 | 0.9876   |
| 0.0006        | 35.45 | 4750 | 0.0842          | 0.9082    | 0.9263 | 0.9172 | 0.9881   |
| 0.0005        | 37.31 | 5000 | 0.0851          | 0.9085    | 0.9266 | 0.9175 | 0.9881   |
| 0.0004        | 39.18 | 5250 | 0.0878          | 0.9035    | 0.9272 | 0.9152 | 0.9879   |
| 0.0004        | 41.04 | 5500 | 0.0856          | 0.9091    | 0.9275 | 0.9182 | 0.9885   |
| 0.0004        | 42.91 | 5750 | 0.0870          | 0.9099    | 0.9255 | 0.9176 | 0.9884   |
| 0.0005        | 44.78 | 6000 | 0.0860          | 0.9010    | 0.9269 | 0.9138 | 0.9882   |
| 0.0004        | 46.64 | 6250 | 0.0851          | 0.9114    | 0.9246 | 0.9179 | 0.9884   |
| 0.0003        | 48.51 | 6500 | 0.0899          | 0.9058    | 0.9252 | 0.9154 | 0.9884   |
| 0.0002        | 50.37 | 6750 | 0.0898          | 0.9050    | 0.9294 | 0.9171 | 0.9882   |
| 0.0002        | 52.24 | 7000 | 0.0890          | 0.9104    | 0.9252 | 0.9177 | 0.9884   |
| 0.0002        | 54.1  | 7250 | 0.0898          | 0.9052    | 0.9260 | 0.9155 | 0.9879   |
| 0.0002        | 55.97 | 7500 | 0.0894          | 0.9080    | 0.9263 | 0.9171 | 0.9883   |
| 0.0001        | 57.84 | 7750 | 0.0910          | 0.9046    | 0.9277 | 0.9160 | 0.9883   |
| 0.0003        | 59.7  | 8000 | 0.0903          | 0.9041    | 0.9283 | 0.9161 | 0.9882   |

### Eval results

|      | Precision | Recall | F1     | Accuracy |
|:----:|:---------:|:------:|:------:|:--------:|
| eval | 0.9116    | 0.9218 | 0.9167 | 0.9883   |
| test | 0.9022    | 0.9107 | 0.9064 | 0.9861   |

### 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 pytorch safetensors roberta token-classification generated_from_trainer Greek ner el license:gpl-3.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #token-classification #generated_from_trainer #Greek #ner #el #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us 

roberta-el-ner4
===============


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: 0.0564
* Precision: 0.9116
* Recall: 0.9218
* F1: 0.9167
* Accuracy: 0.9883


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 #pytorch #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.