🇹🇷 RoBERTaTurk

## Model description
This is a Turkish RoBERTa base model pretrained on Turkish Wikipedia, Turkish OSCAR, and some news websites.

The final training corpus has a size of 38 GB and 329.720.508 sentences.

Thanks to Turkcell we could train the model on Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz 256GB RAM 2 x GV100GL [Tesla V100 PCIe 32GB] GPU for 2.5M steps.

# Usage
Load transformers library with:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
  
tokenizer = AutoTokenizer.from_pretrained("burakaytan/roberta-base-turkish-uncased")
model = AutoModelForMaskedLM.from_pretrained("burakaytan/roberta-base-turkish-uncased")
```


# Fill Mask Usage

```python
from transformers import pipeline

fill_mask = pipeline(
    "fill-mask",
    model="burakaytan/roberta-base-turkish-uncased",
    tokenizer="burakaytan/roberta-base-turkish-uncased"
)

fill_mask("iki ülke arasında <mask> başladı")

[{'sequence': 'iki ülke arasında savaş başladı',
  'score': 0.3013845384120941,
  'token': 1359,
  'token_str': ' savaş'},
 {'sequence': 'iki ülke arasında müzakereler başladı',
  'score': 0.1058429479598999,
  'token': 30439,
  'token_str': ' müzakereler'},
 {'sequence': 'iki ülke arasında görüşmeler başladı',
  'score': 0.07718811184167862,
  'token': 4916,
  'token_str': ' görüşmeler'},
 {'sequence': 'iki ülke arasında kriz başladı',
  'score': 0.07174749672412872,
  'token': 3908,
  'token_str': ' kriz'},
 {'sequence': 'iki ülke arasında çatışmalar başladı',
  'score': 0.05678590387105942,
  'token': 19346,
  'token_str': ' çatışmalar'}]
```
## Citation and Related Information

To cite this model:
```bibtex

@inproceedings{aytan2022comparison,
  title={Comparison of Transformer-Based Models Trained in Turkish and Different Languages on Turkish Natural Language Processing Problems},
  author={Aytan, Burak and Sakar, C Okan},
  booktitle={2022 30th Signal Processing and Communications Applications Conference (SIU)},
  pages={1--4},
  year={2022},
  organization={IEEE}
}

```
{"language": "tr", "license": "mit"}
transformers pytorch roberta fill-mask tr license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fill-mask #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us 

🇹🇷 RoBERTaTurk

## Model description
This is a Turkish RoBERTa base model pretrained on Turkish Wikipedia, Turkish OSCAR, and some news websites.

The final training corpus has a size of 38 GB and 329.720.508 sentences.

Thanks to Turkcell we could train the model on Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz 256GB RAM 2 x GV100GL [Tesla V100 PCIe 32GB] GPU for 2.5M steps.

# Usage
Load transformers library with:



# Fill Mask Usage


and Related Information

To cite this model:

## Model description
This is a Turkish RoBERTa base model pretrained on Turkish Wikipedia, Turkish OSCAR, and some news websites.

The final training corpus has a size of 38 GB and 329.720.508 sentences.

Thanks to Turkcell we could train the model on Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz 256GB RAM 2 x GV100GL [Tesla V100 PCIe 32GB] GPU for 2.5M steps. # Usage
Load transformers library with: # Fill Mask Usage


and Related Information

To cite this model:
TAGS
#transformers #pytorch #roberta #fill-mask #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ## Model description
This is a Turkish RoBERTa base model pretrained on Turkish Wikipedia, Turkish OSCAR, and some news websites.

The final training corpus has a size of 38 GB and 329.720.508 sentences.

Thanks to Turkcell we could train the model on Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz 256GB RAM 2 x GV100GL [Tesla V100 PCIe 32GB] GPU for 2.5M steps. # Usage
Load transformers library with: # Fill Mask Usage


and Related Information

To cite this model:
passage: TAGS
#transformers #pytorch #roberta #fill-mask #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us 
## Model description
This is a Turkish RoBERTa base model pretrained on Turkish Wikipedia, Turkish OSCAR, and some news websites.

The final training corpus has a size of 38 GB and 329.720.508 sentences.

Thanks to Turkcell we could train the model on Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz 256GB RAM 2 x GV100GL [Tesla V100 PCIe 32GB] GPU for 2.5M steps.# Usage
Load transformers library with:# Fill Mask Usage


and Related Information

To cite this model: