

## Greek to English NMT (lower-case output)
## By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)

* source languages: el
* target languages: en
* licence: apache-2.0 
* dataset: Opus, CCmatrix
* model: transformer(fairseq)
* pre-processing: tokenization + BPE segmentation
* metrics: bleu, chrf 
* output: lowercase only, for mixed-cased model use this: https://huggingface.co/lighteternal/SSE-TUC-mt-el-en-cased


### Model description

Trained using the Fairseq framework, transformer_iwslt_de_en architecture.\\
BPE segmentation (10k codes).\\
Lower-case model. 

### How to use

```
from transformers import FSMTTokenizer, FSMTForConditionalGeneration

mname = " <your_downloaded_model_folderpath_here> "

tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

text = "Η τύχη βοηθάει τους τολμηρούς."

encoded = tokenizer.encode(text, return_tensors='pt')

outputs = model.generate(encoded, num_beams=5, num_return_sequences=5, early_stopping=True)
for i, output in enumerate(outputs):
    i += 1
    print(f"{i}: {output.tolist()}")
    
    decoded = tokenizer.decode(output, skip_special_tokens=True)
    print(f"{i}: {decoded}")
```


## Training data

Consolidated corpus from Opus and CC-Matrix (~6.6GB in total)


## Eval results


Results on Tatoeba testset (EL-EN): 

| BLEU | chrF  |
| ------ | ------ |
| 79.3 |  0.795 |


Results on XNLI parallel (EL-EN): 

| BLEU | chrF  |
| ------ | ------ |
| 66.2 |  0.623 |

### BibTeX entry and citation info

Dimitris Papadopoulos, et al. "PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation." (2021). Accepted at EACL 2021 SRW
 

### Acknowledgement

The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)

{"language": ["en", "el"], "license": "apache-2.0", "tags": ["translation"], "metrics": ["bleu"], "widget": [{"text": "\u0397 \u03c4\u03cd\u03c7\u03b7 \u03b2\u03bf\u03b7\u03b8\u03ac\u03b5\u03b9 \u03c4\u03bf\u03c5\u03c2 \u03c4\u03bf\u03bb\u03bc\u03b7\u03c1\u03bf\u03cd\u03c2."}]}
transformers pytorch fsmt text2text-generation translation en el license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #fsmt #text2text-generation #translation #en #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Greek to English NMT (lower-case output)
----------------------------------------


By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
------------------------------------------------------------------------------


* source languages: el
* target languages: en
* licence: apache-2.0
* dataset: Opus, CCmatrix
* model: transformer(fairseq)
* pre-processing: tokenization + BPE segmentation
* metrics: bleu, chrf
* output: lowercase only, for mixed-cased model use this: URL


### Model description


Trained using the Fairseq framework, transformer\_iwslt\_de\_en architecture.\
BPE segmentation (10k codes).\
Lower-case model.


### How to use


Training data
-------------


Consolidated corpus from Opus and CC-Matrix (~6.6GB in total)


Eval results
------------


Results on Tatoeba testset (EL-EN):



Results on XNLI parallel (EL-EN):



### BibTeX entry and citation info


Dimitris Papadopoulos, et al. "PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation." (2021). Accepted at EACL 2021 SRW


### Acknowledgement


The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)



### Model description


Trained using the Fairseq framework, transformer\_iwslt\_de\_en architecture.\
BPE segmentation (10k codes).\
Lower-case model. ### How to use


Training data
-------------


Consolidated corpus from Opus and CC-Matrix (~6.6GB in total)


Eval results
------------


Results on Tatoeba testset (EL-EN):



Results on XNLI parallel (EL-EN): ### BibTeX entry and citation info


Dimitris Papadopoulos, et al. "PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation." (2021). Accepted at EACL 2021 SRW ### Acknowledgement


The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)
TAGS
#transformers #pytorch #fsmt #text2text-generation #translation #en #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Model description


Trained using the Fairseq framework, transformer\_iwslt\_de\_en architecture.\
BPE segmentation (10k codes).\
Lower-case model. ### How to use


Training data
-------------


Consolidated corpus from Opus and CC-Matrix (~6.6GB in total)


Eval results
------------


Results on Tatoeba testset (EL-EN):



Results on XNLI parallel (EL-EN): ### BibTeX entry and citation info


Dimitris Papadopoulos, et al. "PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation." (2021). Accepted at EACL 2021 SRW ### Acknowledgement


The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)
passage: TAGS
#transformers #pytorch #fsmt #text2text-generation #translation #en #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Model description


Trained using the Fairseq framework, transformer\_iwslt\_de\_en architecture.\
BPE segmentation (10k codes).\
Lower-case model.### How to use


Training data
-------------


Consolidated corpus from Opus and CC-Matrix (~6.6GB in total)


Eval results
------------


Results on Tatoeba testset (EL-EN):



Results on XNLI parallel (EL-EN):### BibTeX entry and citation info


Dimitris Papadopoulos, et al. "PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation." (2021). Accepted at EACL 2021 SRW### Acknowledgement


The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)