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# axiothea

This is an experimental roberta model trained with an ancient Greek corpus of about 900 MB, which was scrapped from the web and post-processed. Duplicate texts and editorial punctuation were removed. The training dataset will be soon available in the Huggingface datasets hub. Training a model of ancient Greek is challenging given that it is a low resource language from which 50% of the register has only survived in fragmentary texts. The model is provided by the Diogenet project at the University of California, San Diego. 
 
It achieves the following results on the evaluation set:

- Loss: 3.3351

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step    | Validation Loss |
|:-------------:|:-----:|:-------:|:---------------:|
| 4.7013        | 1.0   | 341422  | 4.8813          |
| 4.2866        | 2.0   | 682844  | 4.4422          |
| 4.0496        | 3.0   | 1024266 | 4.2132          |
| 3.8503        | 4.0   | 1365688 | 4.0246          |
| 3.6917        | 5.0   | 1707110 | 3.8756          |
| 3.4917        | 6.0   | 2048532 | 3.7381          |
| 3.3907        | 7.0   | 2389954 | 3.6107          |
| 3.2876        | 8.0   | 2731376 | 3.5044          |
| 3.1994        | 9.0   | 3072798 | 3.3980          |
| 3.0806        | 10.0  | 3414220 | 3.3095          |


### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.14.0
- Tokenizers 0.10.3

{"language": ["grc"], "tags": ["generated_from_trainer"], "widget": [{"text": "\u03a0\u03bb\u03ac\u03c4\u03c9\u03bd \u1f41 \u03a0\u03b5\u03c1\u03b9\u03ba\u03c4\u03b9\u03cc\u03bd\u03b7\u03c2 <mask> \u03b3\u03ad\u03bd\u03bf\u03c2 \u1f00\u03bd\u03ad\u03c6\u03b5\u03c1\u03b5\u03bd \u03b5\u1f30\u03c2 \u03a3\u03cc\u03bb\u03c9\u03bd\u03b1."}, {"text": "\u1f41 \u039a\u03c1\u03b9\u03c4\u03af\u03b1\u03c2 \u1f00\u03c0\u03ad\u03b2\u03bb\u03b5\u03c8\u03b5 <mask> \u03c4\u1f74\u03bd \u03b8\u03cd\u03c1\u03b1\u03bd."}, {"text": "\u1f6e \u03c6\u03af\u03bb\u03b5 \u039a\u03bb\u03b5\u03b9\u03bd\u03af\u03b1, \u03ba\u03b1\u03bb\u1ff6\u03c2 \u03bc\u1f72\u03bd <mask>."}], "model-index": [{"name": "dioBERTo", "results": []}]}
transformers pytorch roberta fill-mask generated_from_trainer grc autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #grc #autotrain_compatible #endpoints_compatible #region-us 

axiothea
========


This is an experimental roberta model trained with an ancient Greek corpus of about 900 MB, which was scrapped from the web and post-processed. Duplicate texts and editorial punctuation were removed. The training dataset will be soon available in the Huggingface datasets hub. Training a model of ancient Greek is challenging given that it is a low resource language from which 50% of the register has only survived in fragmentary texts. The model is provided by the Diogenet project at the University of California, San Diego.


It achieves the following results on the evaluation set:


* Loss: 3.3351


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10.0


### Training results



### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0+cu102
* Datasets 1.14.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10.0 ### Training results ### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0+cu102
* Datasets 1.14.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #grc #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10.0 ### Training results ### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0+cu102
* Datasets 1.14.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #grc #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10.0### Training results### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0+cu102
* Datasets 1.14.0
* Tokenizers 0.10.3