---
tags:
- summarization
widget:
- text: "protected String renderUri ( URI uri ) { return uri . toASCIIString ( ) ; }"

---

# CodeTrans model for code comment generation java
Pretrained model on programming language java using the t5 small model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java functions.

## Model description

This CodeTrans model is based on the `t5-small` model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets. It is then fine-tuned on the code comment generation task for the java function/method.

## Intended uses & limitations

The model could be used to generate the description for the java function or be fine-tuned on other java code tasks. It can be used on unparsed and untokenized java code. However, if the java code is tokenized, the performance should be better.

### How to use

Here is how to use this model to generate java function documentation using Transformers SummarizationPipeline:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_code_comment_generation_java_multitask_finetune"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_code_comment_generation_java_multitask_finetune", skip_special_tokens=True),
    device=0
)

tokenized_code = "protected String renderUri ( URI uri ) { return uri . toASCIIString ( ) ; }"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/fine-tuning/code%20comment%20generation/small_model.ipynb).
## Training data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)

## Training procedure

### Multi-task Pretraining

The model was trained on a single TPU Pod V3-8 for 260,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Fine-tuning

This model was then fine-tuned on a single TPU Pod V2-8 for 750,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing java code.

## Evaluation results

For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :

|   Language / Model   |      Java      |
| -------------------- | :------------: |
|   CodeTrans-ST-Small    |     37.98      |
|   CodeTrans-ST-Base     |     38.07      |
|   CodeTrans-TF-Small    |     38.56      |
|   CodeTrans-TF-Base     |     39.06      |
|   CodeTrans-TF-Large    |   **39.50**    |
|   CodeTrans-MT-Small    |     20.15      |
|   CodeTrans-MT-Base     |     27.44      |
|   CodeTrans-MT-Large    |     34.69      |
|   CodeTrans-MT-TF-Small |     38.37      |
|   CodeTrans-MT-TF-Base  |     38.90      |
|   CodeTrans-MT-TF-Large |     39.25      |
|   State of the art   |     38.17      |

> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)


---
language: Cszech English
tags:
- translation Cszech English  model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "4) Seznam užívaných výrobků s obsahem PFOS: Kvůli značnému poklesu výroby PFOS po roce 2000 představují největší zdroj emisí patrně dřívější využití, která však nadále reálně existují."

---

# legal_t5_small_trans_cs_en_small_finetuned model

Model on translating legal text from Cszech to English. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three parallel corpus from jrc-acquis, europarl and dcep.

## Model description

legal_t5_small_trans_cs_en_small_finetuned is initially pretrained on unsupervised task with the all of the data of the training set. The unsupervised task was "masked language modelling". legal_t5_small_trans_cs_en_small_finetuned is based on the `t5-small` model and was trained on a large corpus of parallel text. This is a smaller model, which scales the baseline model of t5 down by using `dmodel = 512`, `dff = 2,048`, 8-headed attention, and only 6 layers each in the encoder and decoder. This variant has about 60 million parameters.

## Intended uses & limitations

The model could be used for translation of legal texts from Cszech to English.

### How to use

Here is how to use this model to translate legal text from Cszech to English in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_cs_en_small_finetuned"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_cs_en", do_lower_case=False,
                                            skip_special_tokens=True),
    device=0
)

cs_text = "4) Seznam užívaných výrobků s obsahem PFOS: Kvůli značnému poklesu výroby PFOS po roce 2000 představují největší zdroj emisí patrně dřívější využití, která však nadále reálně existují."

pipeline([cs_text], max_length=512)
```

## Training data

The legal_t5_small_trans_cs_en_small_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 5 Million parallel texts.

## Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

### Pretraining

The pre-training data was the combined data from all the 42 language pairs. The task for the model was to predict the portions of a sentence which were masked randomly.

## Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_trans_cs_en_small_finetuned | 56.936|

### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)


---
tags:
- summarization
widget:
- text: "you are given an array of numbers a and a number b , compute the difference of elements in a and b"

---

# CodeTrans model for program synthesis
Pretrained model on programming language lisp inspired DSL using the t5 large model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans).

## Model description

This CodeTrans model is based on the `t5-large` model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets. It is then fine-tuned on the program synthesis task for the lisp inspired DSL code.

## Intended uses & limitations

The model could be used to generate lisp inspired DSL code given the human language description tasks.

### How to use

Here is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_large_program_synthese_multitask_finetune"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_program_synthese_multitask_finetune", skip_special_tokens=True),
    device=0
)

tokenized_code = "you are given an array of numbers a and a number b , compute the difference of elements in a and b"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/fine-tuning/program%20synthesis/large_model.ipynb).
## Training data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)

## Training procedure

### Multi-task Pretraining

The model was trained on a single TPU Pod V3-8 for 500,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Fine-tuning

This model was then fine-tuned on a single TPU Pod V2-8 for 2,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing lisp inspired DSL data.

## Evaluation results

For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :

|   Language / Model   |      LISP      |
| -------------------- | :------------: |
|   CodeTrans-ST-Small    |     89.43      |
|   CodeTrans-ST-Base     |     89.65      |
|   CodeTrans-TF-Small    |     90.30      |
|   CodeTrans-TF-Base     |     90.24      |
|   CodeTrans-TF-Large    |     90.21      |
|   CodeTrans-MT-Small    |     82.88      |
|   CodeTrans-MT-Base     |     86.99      |
|   CodeTrans-MT-Large    |     90.27      |
|   CodeTrans-MT-TF-Small |   **90.31**    |
|   CodeTrans-MT-TF-Base  |     90.30      |
|   CodeTrans-MT-TF-Large |     90.17      |
|   State of the art   |     85.80      |

> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)


---
tags:
- summarization
widget:
- text: "parse the uses licence node of this package , if any , and returns the license definition if theres"

---

# CodeTrans model for api recommendation generation
Pretrained model for api recommendation generation using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans).

## Model description

This CodeTrans model is based on the `t5-base` model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets. It is then fine-tuned on the api recommendation generation task for the java apis.

## Intended uses & limitations

The model could be used to generate api usage for the java programming tasks.

### How to use

Here is how to use this model to generate java function documentation using Transformers SummarizationPipeline:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_api_generation_multitask_finetune"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_api_generation_multitask_finetune", skip_special_tokens=True),
    device=0
)

tokenized_code = "parse the uses licence node of this package , if any , and returns the license definition if theres"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/fine-tuning/api%20generation/base_model.ipynb).
## Training data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)

## Training procedure

### Multi-task Pretraining

The model was trained on a single TPU Pod V3-8 for 500,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Fine-tuning

This model was then fine-tuned on a single TPU Pod V2-8 for 320,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing api recommendation generation data.

## Evaluation results

For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :

|   Language / Model   |      Java      |
| -------------------- | :------------: |
|   CodeTrans-ST-Small    |     68.71      |
|   CodeTrans-ST-Base     |     70.45      |
|   CodeTrans-TF-Small    |     68.90      |
|   CodeTrans-TF-Base     |     72.11      |
|   CodeTrans-TF-Large    |     73.26      |
|   CodeTrans-MT-Small    |     58.43      |
|   CodeTrans-MT-Base     |     67.97      |
|   CodeTrans-MT-Large    |     72.29      |
|   CodeTrans-MT-TF-Small |     69.29      |
|   CodeTrans-MT-TF-Base  |     72.89      |
|   CodeTrans-MT-TF-Large |   **73.39**    |
|   State of the art   |     54.42      |

> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)


---
tags:
- summarization
widget:
- text: "func ( pr * Progress ) needSnapshotAbort ( ) bool { return pr . State == ProgressStateSnapshot && pr . Match >= pr . PendingSnapshot   }"

---

# CodeTrans model for code documentation generation go
Pretrained model on programming language go using the t5 small model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized go code functions: it works best with tokenized go functions.

## Model description

This CodeTrans model is based on the `t5-small` model. It has its own SentencePiece vocabulary model. It used transfer-learning pre-training on 7 unsupervised datasets in the software development domain. It is then fine-tuned on the code documentation generation task for the go function/method.

## Intended uses & limitations

The model could be used to generate the description for the go function or be fine-tuned on other go code tasks. It can be used on unparsed and untokenized go code. However, if the go code is tokenized, the performance should be better.

### How to use

Here is how to use this model to generate go function documentation using Transformers SummarizationPipeline:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_code_documentation_generation_go_transfer_learning_finetune"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_code_documentation_generation_go_transfer_learning_finetune", skip_special_tokens=True),
    device=0
)

tokenized_code = "func ( pr * Progress ) needSnapshotAbort ( ) bool { return pr . State == ProgressStateSnapshot && pr . Match >= pr . PendingSnapshot   }"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/transfer%20learning%20fine-tuning/function%20documentation%20generation/go/small_model.ipynb).
## Training data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)

## Training procedure

### Transfer-learning Pretraining

The model was trained on a single TPU Pod V3-8 for half million steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Fine-tuning

This model was then fine-tuned on a single TPU Pod V2-8 for 10,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing go code.

## Evaluation results

For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :

|   Language / Model   |     Python     |      Java      |       Go       |      Php       |      Ruby      |   JavaScript   |
| -------------------- | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: |
|   CodeTrans-ST-Small    |      17.31     |     16.65      |     16.89      |     23.05      |      9.19      |      13.7      |
|   CodeTrans-ST-Base     |      16.86     |     17.17      |     17.16      |     22.98      |      8.23      |      13.17     |
|   CodeTrans-TF-Small    |      19.93     |     19.48      |     18.88      |     25.35      |     13.15      |      17.23     |
|   CodeTrans-TF-Base     |      20.26     |     20.19      |     19.50      |     25.84      |     14.07      |      18.25     |
|   CodeTrans-TF-Large    |      20.35     |     20.06      |   **19.54**    |     26.18      |     14.94      |    **18.98**   |
|   CodeTrans-MT-Small    |      19.64     |     19.00      |     19.15      |     24.68      |     14.91      |      15.26     |
|   CodeTrans-MT-Base     |    **20.39**   |     21.22      |     19.43      |   **26.23**    |   **15.26**    |      16.11     |
|   CodeTrans-MT-Large    |      20.18     |   **21.87**    |     19.38      |     26.08      |     15.00      |      16.23     |
|   CodeTrans-MT-TF-Small |      19.77     |     20.04      |     19.36      |     25.55      |     13.70      |      17.24     |
|   CodeTrans-MT-TF-Base  |      19.77     |     21.12      |     18.86      |     25.79      |     14.24      |      18.62     |
|   CodeTrans-MT-TF-Large |      18.94     |     21.42      |     18.77      |     26.20      |     14.19      |      18.83     |
|   State of the art   |      19.06     |     17.65      |     18.07      |     25.16      |     12.16      |      14.90     |

> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)


---
tags:
- summarization
widget:
- text: "parse the uses licence node of this package , if any , and returns the license definition if theres"

---

# CodeTrans model for api recommendation generation
Pretrained model for api recommendation generation using the t5 small model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans).

## Model description

This CodeTrans model is based on the `t5-small` model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets.

## Intended uses & limitations

The model could be used to generate api usage for the java programming tasks.

### How to use

Here is how to use this model to generate java function documentation using Transformers SummarizationPipeline:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_api_generation_multitask"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_api_generation_multitask", skip_special_tokens=True),
    device=0
)

tokenized_code = "parse the uses licence node of this package , if any , and returns the license definition if theres"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/pre-training/api%20generation/small_model.ipynb).
## Training data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)

## Training procedure

### Multi-task Pretraining

The model was trained on a single TPU Pod V3-8 for 500,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

## Evaluation results

For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :

|   Language / Model   |      Java      |
| -------------------- | :------------: |
|   CodeTrans-ST-Small    |     68.71      |
|   CodeTrans-ST-Base     |     70.45      |
|   CodeTrans-TF-Small    |     68.90      |
|   CodeTrans-TF-Base     |     72.11      |
|   CodeTrans-TF-Large    |     73.26      |
|   CodeTrans-MT-Small    |     58.43      |
|   CodeTrans-MT-Base     |     67.97      |
|   CodeTrans-MT-Large    |     72.29      |
|   CodeTrans-MT-TF-Small |     69.29      |
|   CodeTrans-MT-TF-Base  |     72.89      |
|   CodeTrans-MT-TF-Large |   **73.39**    |
|   State of the art   |     54.42      |

> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)


function documentation generation python:

---
tags:
- summarization
widget:
- text: "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )"

---

# CodeTrans model for code documentation generation python
Pretrained model on programming language python using the t5 small model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized python code functions: it works best with tokenized python functions.

## Model description

This CodeTrans model is based on the `t5-small` model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets. It is then fine-tuned on the code documentation generation task for the python function/method.

## Intended uses & limitations

The model could be used to generate the description for the python function or be fine-tuned on other python code tasks. It can be used on unparsed and untokenized python code. However, if the python code is tokenized, the performance should be better.

### How to use

Here is how to use this model to generate python function documentation using Transformers SummarizationPipeline:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_code_documentation_generation_python_multitask_finetune"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_code_documentation_generation_python_multitask_finetune", skip_special_tokens=True),
    device=0
)

tokenized_code = "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/fine-tuning/function%20documentation%20generation/python/small_model.ipynb).
## Training data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)

## Training procedure

### Multi-task Pretraining

The model was trained on a single TPU Pod V3-8 for half million steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Fine-tuning

This model was then fine-tuned on a single TPU Pod V2-8 for 4000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing python code.

## Evaluation results

For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :

|   Language / Model   |     Python     |      Java      |       Go       |      Php       |      Ruby      |   JavaScript   |
| -------------------- | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: |
|   CodeTrans-ST-Small    |      17.31     |     16.65      |     16.89      |     23.05      |      9.19      |      13.7      |
|   CodeTrans-ST-Base     |      16.86     |     17.17      |     17.16      |     22.98      |      8.23      |      13.17     |
|   CodeTrans-TF-Small    |      19.93     |     19.48      |     18.88      |     25.35      |     13.15      |      17.23     |
|   CodeTrans-TF-Base     |      20.26     |     20.19      |     19.50      |     25.84      |     14.07      |      18.25     |
|   CodeTrans-TF-Large    |      20.35     |     20.06      |   **19.54**    |     26.18      |     14.94      |    **18.98**   |
|   CodeTrans-MT-Small    |      19.64     |     19.00      |     19.15      |     24.68      |     14.91      |      15.26     |
|   CodeTrans-MT-Base     |    **20.39**   |     21.22      |     19.43      |   **26.23**    |   **15.26**    |      16.11     |
|   CodeTrans-MT-Large    |      20.18     |   **21.87**    |     19.38      |     26.08      |     15.00      |      16.23     |
|   CodeTrans-MT-TF-Small |      19.77     |     20.04      |     19.36      |     25.55      |     13.70      |      17.24     |
|   CodeTrans-MT-TF-Base  |      19.77     |     21.12      |     18.86      |     25.79      |     14.24      |      18.62     |
|   CodeTrans-MT-TF-Large |      18.94     |     21.42      |     18.77      |     26.20      |     14.19      |      18.83     |
|   State of the art   |      19.06     |     17.65      |     18.07      |     25.16      |     12.16      |      14.90     |

> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)


---
tags:
- summarization
widget:
- text: "func ( pr * Progress ) needSnapshotAbort ( ) bool { return pr . State == ProgressStateSnapshot && pr . Match >= pr . PendingSnapshot   }"

---

# CodeTrans model for code documentation generation go
Pretrained model on programming language go using the t5 large model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized go code functions: it works best with tokenized go functions.

## Model description

This CodeTrans model is based on the `t5-large` model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets.

## Intended uses & limitations

The model could be used to generate the description for the go function or be fine-tuned on other go code tasks. It can be used on unparsed and untokenized go code. However, if the go code is tokenized, the performance should be better.

### How to use

Here is how to use this model to generate go function documentation using Transformers SummarizationPipeline:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_large_code_documentation_generation_go_multitask"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_code_documentation_generation_go_multitask", skip_special_tokens=True),
    device=0
)

tokenized_code = "func ( pr * Progress ) needSnapshotAbort ( ) bool { return pr . State == ProgressStateSnapshot && pr . Match >= pr . PendingSnapshot   }"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/pre-training/function%20documentation%20generation/go/large_model.ipynb).
## Training data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)

## Training procedure

### Multi-task Pretraining

The model was trained on a single TPU Pod V3-8 for 180,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

## Evaluation results

For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :

|   Language / Model   |     Python     |      Java      |       Go       |      Php       |      Ruby      |   JavaScript   |
| -------------------- | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: |
|   CodeTrans-ST-Small    |      17.31     |     16.65      |     16.89      |     23.05      |      9.19      |      13.7      |
|   CodeTrans-ST-Base     |      16.86     |     17.17      |     17.16      |     22.98      |      8.23      |      13.17     |
|   CodeTrans-TF-Small    |      19.93     |     19.48      |     18.88      |     25.35      |     13.15      |      17.23     |
|   CodeTrans-TF-Base     |      20.26     |     20.19      |     19.50      |     25.84      |     14.07      |      18.25     |
|   CodeTrans-TF-Large    |      20.35     |     20.06      |   **19.54**    |     26.18      |     14.94      |    **18.98**   |
|   CodeTrans-MT-Small    |      19.64     |     19.00      |     19.15      |     24.68      |     14.91      |      15.26     |
|   CodeTrans-MT-Base     |    **20.39**   |     21.22      |     19.43      |   **26.23**    |   **15.26**    |      16.11     |
|   CodeTrans-MT-Large    |      20.18     |   **21.87**    |     19.38      |     26.08      |     15.00      |      16.23     |
|   CodeTrans-MT-TF-Small |      19.77     |     20.04      |     19.36      |     25.55      |     13.70      |      17.24     |
|   CodeTrans-MT-TF-Base  |      19.77     |     21.12      |     18.86      |     25.79      |     14.24      |      18.62     |
|   CodeTrans-MT-TF-Large |      18.94     |     21.42      |     18.77      |     26.20      |     14.19      |      18.83     |
|   State of the art   |      19.06     |     17.65      |     18.07      |     25.16      |     12.16      |      14.90     |

> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)


---
language: Swedish English
tags:
- translation Swedish English  model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "Alejo Vidal-Quadras : 262 röster"

---

# legal_t5_small_trans_sv_en_small_finetuned model

Model on translating legal text from Swedish to English. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three parallel corpus from jrc-acquis, europarl and dcep.

## Model description

legal_t5_small_trans_sv_en_small_finetuned is initially pretrained on unsupervised task with the all of the data of the training set. The unsupervised task was "masked language modelling". legal_t5_small_trans_sv_en_small_finetuned is based on the `t5-small` model and was trained on a large corpus of parallel text. This is a smaller model, which scales the baseline model of t5 down by using `dmodel = 512`, `dff = 2,048`, 8-headed attention, and only 6 layers each in the encoder and decoder. This variant has about 60 million parameters.

## Intended uses & limitations

The model could be used for translation of legal texts from Swedish to English.

### How to use

Here is how to use this model to translate legal text from Swedish to English in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_sv_en_small_finetuned"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_sv_en", do_lower_case=False,
                                            skip_special_tokens=True),
    device=0
)

sv_text = "Alejo Vidal-Quadras : 262 röster"

pipeline([sv_text], max_length=512)
```

## Training data

The legal_t5_small_trans_sv_en_small_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 9 Million parallel texts.

## Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

### Pretraining

The pre-training data was the combined data from all the 42 language pairs. The task for the model was to predict the portions of a sentence which were masked randomly.

## Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_trans_sv_en_small_finetuned | 52.084|

### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)


---
language: French Cszech
tags:
- translation French Cszech  model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "Compte rendu de la délégation à la Convention-cadre des Nations unies sur le changement climatique (COP17) à Durban (Afrique du Sud)"

---

# legal_t5_small_trans_fr_cs_small_finetuned model

Model on translating legal text from French to Cszech. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three parallel corpus from jrc-acquis, europarl and dcep.

## Model description

legal_t5_small_trans_fr_cs_small_finetuned is initially pretrained on unsupervised task with the all of the data of the training set. The unsupervised task was "masked language modelling". legal_t5_small_trans_fr_cs_small_finetuned is based on the `t5-small` model and was trained on a large corpus of parallel text. This is a smaller model, which scales the baseline model of t5 down by using `dmodel = 512`, `dff = 2,048`, 8-headed attention, and only 6 layers each in the encoder and decoder. This variant has about 60 million parameters.

## Intended uses & limitations

The model could be used for translation of legal texts from French to Cszech.

### How to use

Here is how to use this model to translate legal text from French to Cszech in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_fr_cs_small_finetuned"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_fr_cs", do_lower_case=False,
                                            skip_special_tokens=True),
    device=0
)

fr_text = "Compte rendu de la délégation à la Convention-cadre des Nations unies sur le changement climatique (COP17) à Durban (Afrique du Sud)"

pipeline([fr_text], max_length=512)
```

## Training data

The legal_t5_small_trans_fr_cs_small_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 5 Million parallel texts.

## Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

### Pretraining

The pre-training data was the combined data from all the 42 language pairs. The task for the model was to predict the portions of a sentence which were masked randomly.

## Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_trans_fr_cs_small_finetuned | 44.410|

### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)


---
tags:
- summarization
widget:
- text: "parse the uses licence node of this package , if any , and returns the license definition if theres"

---

# CodeTrans model for api recommendation generation
Pretrained model for api recommendation generation using the t5 large model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans).

## Model description

This CodeTrans model is based on the `t5-large` model. It has its own SentencePiece vocabulary model. It used transfer-learning pre-training on 7 unsupervised datasets in the software development domain. It is then fine-tuned on the api recommendation generation task for the java apis.

## Intended uses & limitations

The model could be used to generate api usage for the java programming tasks.

### How to use

Here is how to use this model to generate java function documentation using Transformers SummarizationPipeline:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_large_api_generation_transfer_learning_finetune"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_api_generation_transfer_learning_finetune", skip_special_tokens=True),
    device=0
)

tokenized_code = "parse the uses licence node of this package , if any , and returns the license definition if theres"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/transfer%20learning%20fine-tuning/api%20generation/large_model.ipynb).
## Training data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)

## Training procedure

### Transfer-learning Pretraining

The model was trained on a single TPU Pod V3-8 for 240,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Fine-tuning

This model was then fine-tuned on a single TPU Pod V3-8 for 180,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing api recommendation generation data.

## Evaluation results

For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :

|   Language / Model   |      Java      |
| -------------------- | :------------: |
|   CodeTrans-ST-Small    |     68.71      |
|   CodeTrans-ST-Base     |     70.45      |
|   CodeTrans-TF-Small    |     68.90      |
|   CodeTrans-TF-Base     |     72.11      |
|   CodeTrans-TF-Large    |     73.26      |
|   CodeTrans-MT-Small    |     58.43      |
|   CodeTrans-MT-Base     |     67.97      |
|   CodeTrans-MT-Large    |     72.29      |
|   CodeTrans-MT-TF-Small |     69.29      |
|   CodeTrans-MT-TF-Base  |     72.89      |
|   CodeTrans-MT-TF-Large |   **73.39**    |
|   State of the art   |     54.42      |

> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)


---
language: French Swedish
tags:
- translation French Swedish  model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "posée conformément à l'article 43 du règlement"

---

# legal_t5_small_trans_fr_sv model

Model on translating legal text from French to Swedish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.

## Model description

legal_t5_small_trans_fr_sv is based on the `t5-small` model and was trained on a large corpus of parallel text. This is a smaller model, which scales the baseline model of t5 down by using `dmodel = 512`, `dff = 2,048`, 8-headed attention, and only 6 layers each in the encoder and decoder. This variant has about 60 million parameters.

## Intended uses & limitations

The model could be used for translation of legal texts from French to Swedish.

### How to use

Here is how to use this model to translate legal text from French to Swedish in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_fr_sv"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_fr_sv", do_lower_case=False,
                                            skip_special_tokens=True),
    device=0
)

fr_text = "posée conformément à l'article 43 du règlement"

pipeline([fr_text], max_length=512)
```

## Training data

The legal_t5_small_trans_fr_sv model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 5 Million parallel texts.

## Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

### Pretraining

## Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_trans_fr_sv | 41.9|

### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)


---
language: Spanish Swedish
tags:
- translation Spanish Swedish  model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "Marie Anne Isler Béguin ,"

---

# legal_t5_small_trans_es_sv_small_finetuned model

Model on translating legal text from Spanish to Swedish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three parallel corpus from jrc-acquis, europarl and dcep.

## Model description

legal_t5_small_trans_es_sv_small_finetuned is initially pretrained on unsupervised task with the all of the data of the training set. The unsupervised task was "masked language modelling". legal_t5_small_trans_es_sv_small_finetuned is based on the `t5-small` model and was trained on a large corpus of parallel text. This is a smaller model, which scales the baseline model of t5 down by using `dmodel = 512`, `dff = 2,048`, 8-headed attention, and only 6 layers each in the encoder and decoder. This variant has about 60 million parameters.

## Intended uses & limitations

The model could be used for translation of legal texts from Spanish to Swedish.

### How to use

Here is how to use this model to translate legal text from Spanish to Swedish in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_es_sv_small_finetuned"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_es_sv", do_lower_case=False,
                                            skip_special_tokens=True),
    device=0
)

es_text = "Marie Anne Isler Béguin ,"

pipeline([es_text], max_length=512)
```

## Training data

The legal_t5_small_trans_es_sv_small_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 8 Million parallel texts.

## Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

### Pretraining

The pre-training data was the combined data from all the 42 language pairs. The task for the model was to predict the portions of a sentence which were masked randomly.

## Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_trans_es_sv_small_finetuned | 43.838|

### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)


---
tags:
- summarization
widget:
- text: "public static DateTime ParseUnixDateTime ( double unixTime ) { var dt = new DateTime ( CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , System . DateTimeKind . Utc ) ; dt = dt . AddSeconds ( unixTimeStamp ) . ToLocalTime ( ) ; return dt ; }"

---

# CodeTrans model for source code summarization csharp
Pretrained model on programming language csharp using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized csharp code functions: it works best with tokenized csharp functions.

## Model description

This CodeTrans model is based on the `t5-base` model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets.

## Intended uses & limitations

The model could be used to generate the description for the csharp function or be fine-tuned on other csharp code tasks. It can be used on unparsed and untokenized csharp code. However, if the csharp code is tokenized, the performance should be better.

### How to use

Here is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask", skip_special_tokens=True),
    device=0
)

tokenized_code = "public static DateTime ParseUnixDateTime ( double unixTime ) { var dt = new DateTime ( CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , System . DateTimeKind . Utc ) ; dt = dt . AddSeconds ( unixTimeStamp ) . ToLocalTime ( ) ; return dt ; }"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/pre-training/source%20code%20summarization/csharp/base_model.ipynb).
## Training data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)

## Training procedure

### Multi-task Pretraining

The model was trained on a single TPU Pod V3-8 for 160,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

## Evaluation results

For the source code summarization tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :

|   Language / Model   |     Python     |       SQL      |       C#       |
| -------------------- | :------------: | :------------: | :------------: |
|   CodeTrans-ST-Small    |      8.45      |     17.55      |     19.74      |
|   CodeTrans-ST-Base     |      9.12      |     15.00      |     18.65      |
|   CodeTrans-TF-Small    |     10.06      |     17.71      |     20.40      |
|   CodeTrans-TF-Base     |     10.94      |     17.66      |     21.12      |
|   CodeTrans-TF-Large    |     12.41      |     18.40      |     21.43      |
|   CodeTrans-MT-Small    |     13.11      |     19.15      |     22.39      |
|   CodeTrans-MT-Base     |   **13.37**    |     19.24      |     23.20      |
|   CodeTrans-MT-Large    |     13.24      |     19.40      |   **23.57**    |
|   CodeTrans-MT-TF-Small |     12.10      |     18.25      |     22.03      |
|   CodeTrans-MT-TF-Base  |     10.64      |     16.91      |     21.40      |
|   CodeTrans-MT-TF-Large |     12.14      |   **19.98**    |     21.10      |
|   CODE-NN   |       --       |     18.40      |     20.50      |

> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)


---
language: Deustch French
tags:
- translation Deustch French  model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "SCHRIFTLICHE ANFRAGE P-0029/06"

---

# legal_t5_small_trans_de_fr_small_finetuned model

Model on translating legal text from Deustch to French. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three parallel corpus from jrc-acquis, europarl and dcep.

## Model description

legal_t5_small_trans_de_fr_small_finetuned is initially pretrained on unsupervised task with the all of the data of the training set. The unsupervised task was "masked language modelling". legal_t5_small_trans_de_fr_small_finetuned is based on the `t5-small` model and was trained on a large corpus of parallel text. This is a smaller model, which scales the baseline model of t5 down by using `dmodel = 512`, `dff = 2,048`, 8-headed attention, and only 6 layers each in the encoder and decoder. This variant has about 60 million parameters.

## Intended uses & limitations

The model could be used for translation of legal texts from Deustch to French.

### How to use

Here is how to use this model to translate legal text from Deustch to French in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_de_fr_small_finetuned"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_de_fr", do_lower_case=False,
                                            skip_special_tokens=True),
    device=0
)

de_text = "SCHRIFTLICHE ANFRAGE P-0029/06"

pipeline([de_text], max_length=512)
```

## Training data

The legal_t5_small_trans_de_fr_small_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 8 Million parallel texts.

## Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

### Pretraining

The pre-training data was the combined data from all the 42 language pairs. The task for the model was to predict the portions of a sentence which were masked randomly.

## Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_trans_de_fr_small_finetuned | 47.461|

### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)


---
tags:
- summarization
widget:
- text: "public static DateTime ParseUnixDateTime ( double unixTime ) { var dt = new DateTime ( CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , System . DateTimeKind . Utc ) ; dt = dt . AddSeconds ( unixTimeStamp ) . ToLocalTime ( ) ; return dt ; }"

---

# CodeTrans model for source code summarization csharp
Pretrained model on programming language csharp using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized csharp code functions: it works best with tokenized csharp functions.

## Model description

This CodeTrans model is based on the `t5-base` model. It has its own SentencePiece vocabulary model. It used transfer-learning pre-training on 7 unsupervised datasets in the software development domain. It is then fine-tuned on the source code summarization task for the csharp code snippets.

## Intended uses & limitations

The model could be used to generate the description for the csharp function or be fine-tuned on other csharp code tasks. It can be used on unparsed and untokenized csharp code. However, if the csharp code is tokenized, the performance should be better.

### How to use

Here is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_csharp_transfer_learning_finetune"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_csharp_transfer_learning_finetune", skip_special_tokens=True),
    device=0
)

tokenized_code =  "public static DateTime ParseUnixDateTime ( double unixTime ) { var dt = new DateTime ( CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , System . DateTimeKind . Utc ) ; dt = dt . AddSeconds ( unixTimeStamp ) . ToLocalTime ( ) ; return dt ; }"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/transfer%20learning%20fine-tuning/source%20code%20summarization/csharp/base_model.ipynb).
## Training data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)

## Training procedure

### Transfer-learning Pretraining

The model was trained on a single TPU Pod V3-8 for 500,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Fine-tuning

This model was then fine-tuned on a single TPU Pod V2-8 for 500 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing csharp code.

## Evaluation results

For the source code summarization tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :

|   Language / Model   |     Python     |       SQL      |       C#       |
| -------------------- | :------------: | :------------: | :------------: |
|   CodeTrans-ST-Small    |      8.45      |     17.55      |     19.74      |
|   CodeTrans-ST-Base     |      9.12      |     15.00      |     18.65      |
|   CodeTrans-TF-Small    |     10.06      |     17.71      |     20.40      |
|   CodeTrans-TF-Base     |     10.94      |     17.66      |     21.12      |
|   CodeTrans-TF-Large    |     12.41      |     18.40      |     21.43      |
|   CodeTrans-MT-Small    |     13.11      |     19.15      |     22.39      |
|   CodeTrans-MT-Base     |   **13.37**    |     19.24      |     23.20      |
|   CodeTrans-MT-Large    |     13.24      |     19.40      |   **23.57**    |
|   CodeTrans-MT-TF-Small |     12.10      |     18.25      |     22.03      |
|   CodeTrans-MT-TF-Base  |     10.64      |     16.91      |     21.40      |
|   CodeTrans-MT-TF-Large |     12.14      |   **19.98**    |     21.10      |
|   CODE-NN   |       --       |     18.40      |     20.50      |

> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)


---
language: Italian English
tags:
- translation Italian English  model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "Supplenti presenti al momento della votazione finale"

---

# legal_t5_small_trans_it_en_small_finetuned model

Model on translating legal text from Italian to English. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three parallel corpus from jrc-acquis, europarl and dcep.

## Model description

legal_t5_small_trans_it_en_small_finetuned is initially pretrained on unsupervised task with the all of the data of the training set. The unsupervised task was "masked language modelling". legal_t5_small_trans_it_en_small_finetuned is based on the `t5-small` model and was trained on a large corpus of parallel text. This is a smaller model, which scales the baseline model of t5 down by using `dmodel = 512`, `dff = 2,048`, 8-headed attention, and only 6 layers each in the encoder and decoder. This variant has about 60 million parameters.

## Intended uses & limitations

The model could be used for translation of legal texts from Italian to English.

### How to use

Here is how to use this model to translate legal text from Italian to English in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_it_en_small_finetuned"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_it_en", do_lower_case=False,
                                            skip_special_tokens=True),
    device=0
)

it_text = "Supplenti presenti al momento della votazione finale"

pipeline([it_text], max_length=512)
```

## Training data

The legal_t5_small_trans_it_en_small_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 9 Million parallel texts.

## Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

### Pretraining

The pre-training data was the combined data from all the 42 language pairs. The task for the model was to predict the portions of a sentence which were masked randomly.

## Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_trans_it_en_small_finetuned | 49.840|

### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)


---
tags:
- summarization
widget:
- text: "protected String renderUri ( URI uri ) { return uri . toASCIIString ( ) ; }"

---

# CodeTrans model for code comment generation java
Pretrained model on programming language java using the t5 large model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java functions.

## Model description

This CodeTrans model is based on the `t5-large` model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets.

## Intended uses & limitations

The model could be used to generate the description for the java function or be fine-tuned on other java code tasks. It can be used on unparsed and untokenized java code. However, if the java code is tokenized, the performance should be better.

### How to use

Here is how to use this model to generate java function documentation using Transformers SummarizationPipeline:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_large_code_comment_generation_java_multitask"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_code_comment_generation_java_multitask", skip_special_tokens=True),
    device=0
)

tokenized_code = "protected String renderUri ( URI uri ) { return uri . toASCIIString ( ) ; }"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/pre-training/code%20comment%20generation/large_model.ipynb).
## Training data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)

## Training procedure

### Multi-task Pretraining

The model was trained on a single TPU Pod V3-8 for 260,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

## Evaluation results

For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :

|   Language / Model   |      Java      |
| -------------------- | :------------: |
|   CodeTrans-ST-Small    |     37.98      |
|   CodeTrans-ST-Base     |     38.07      |
|   CodeTrans-TF-Small    |     38.56      |
|   CodeTrans-TF-Base     |     39.06      |
|   CodeTrans-TF-Large    |   **39.50**    |
|   CodeTrans-MT-Small    |     20.15      |
|   CodeTrans-MT-Base     |     27.44      |
|   CodeTrans-MT-Large    |     34.69      |
|   CodeTrans-MT-TF-Small |     38.37      |
|   CodeTrans-MT-TF-Base  |     38.90      |
|   CodeTrans-MT-TF-Large |     39.25      |
|   State of the art   |     38.17      |

> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)


---
tags:
- summarization
widget:
- text: '''with open ( CODE_STRING , CODE_STRING ) as in_file : buf = in_file . readlines ( )  with open ( CODE_STRING , CODE_STRING ) as out_file : for line in buf :          if line ==   " ; Include this text   " :              line = line +   " Include below  "          out_file . write ( line ) '''

---

# CodeTrans model for source code summarization python
Pretrained model on programming language python using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized python code functions: it works best with tokenized python functions.

## Model description

This CodeTrans model is based on the `t5-base` model. It has its own SentencePiece vocabulary model. It used transfer-learning pre-training on 7 unsupervised datasets in the software development domain. It is then fine-tuned on the source code summarization task for the python code snippets.

## Intended uses & limitations

The model could be used to generate the description for the python function or be fine-tuned on other python code tasks. It can be used on unparsed and untokenized python code. However, if the python code is tokenized, the performance should be better.

### How to use

Here is how to use this model to generate python function documentation using Transformers SummarizationPipeline:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_python_transfer_learning_finetune"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_python_transfer_learning_finetune", skip_special_tokens=True),
    device=0
)

tokenized_code =  '''with open ( CODE_STRING , CODE_STRING ) as in_file : buf = in_file . readlines ( )  with open ( CODE_STRING , CODE_STRING ) as out_file : for line in buf :          if line ==   " ; Include this text   " :              line = line +   " Include below  "          out_file . write ( line ) '''
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/transfer%20learning%20fine-tuning/source%20code%20summarization/python/base_model.ipynb).
## Training data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)

## Training procedure

### Transfer-learning Pretraining

The model was trained on a single TPU Pod V3-8 for 500,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Fine-tuning

This model was then fine-tuned on a single TPU Pod V2-8 for 1000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing python code.

## Evaluation results

For the source code summarization tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :

|   Language / Model   |     Python     |       SQL      |       C#       |
| -------------------- | :------------: | :------------: | :------------: |
|   CodeTrans-ST-Small    |      8.45      |     17.55      |     19.74      |
|   CodeTrans-ST-Base     |      9.12      |     15.00      |     18.65      |
|   CodeTrans-TF-Small    |     10.06      |     17.71      |     20.40      |
|   CodeTrans-TF-Base     |     10.94      |     17.66      |     21.12      |
|   CodeTrans-TF-Large    |     12.41      |     18.40      |     21.43      |
|   CodeTrans-MT-Small    |     13.11      |     19.15      |     22.39      |
|   CodeTrans-MT-Base     |   **13.37**    |     19.24      |     23.20      |
|   CodeTrans-MT-Large    |     13.24      |     19.40      |   **23.57**    |
|   CodeTrans-MT-TF-Small |     12.10      |     18.25      |     22.03      |
|   CodeTrans-MT-TF-Base  |     10.64      |     16.91      |     21.40      |
|   CodeTrans-MT-TF-Large |     12.14      |   **19.98**    |     21.10      |
|   CODE-NN   |       --       |     18.40      |     20.50      |

> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)


---
language: Italian Swedish
tags:
- translation Italian Swedish  model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "K. considerando che, come avviene con tutti i sistemi di sanità elettronica, la progettazione, lo sviluppo e l’attuazione di sistemi abilitati alla tecnologia RFID presuppongono il coinvolgimento diretto dei professionisti sanitari, dei pazienti e delle commissioni competenti (per esempio, sulla protezione dei dati e sull’etica),"

---

# legal_t5_small_trans_it_sv model

Model on translating legal text from Italian to Swedish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.

## Model description

legal_t5_small_trans_it_sv is based on the `t5-small` model and was trained on a large corpus of parallel text. This is a smaller model, which scales the baseline model of t5 down by using `dmodel = 512`, `dff = 2,048`, 8-headed attention, and only 6 layers each in the encoder and decoder. This variant has about 60 million parameters.

## Intended uses & limitations

The model could be used for translation of legal texts from Italian to Swedish.

### How to use

Here is how to use this model to translate legal text from Italian to Swedish in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_it_sv"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_it_sv", do_lower_case=False,
                                            skip_special_tokens=True),
    device=0
)

it_text = "K. considerando che, come avviene con tutti i sistemi di sanità elettronica, la progettazione, lo sviluppo e l’attuazione di sistemi abilitati alla tecnologia RFID presuppongono il coinvolgimento diretto dei professionisti sanitari, dei pazienti e delle commissioni competenti (per esempio, sulla protezione dei dati e sull’etica),"

pipeline([it_text], max_length=512)
```

## Training data

The legal_t5_small_trans_it_sv model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 5 Million parallel texts.

## Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

### Pretraining

## Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_trans_it_sv | 41.508|

### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)


---
language: Deustch Italian
tags:
- translation Deustch Italian  model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "sicherstellen, dass alle Bürger gemäß der Richtlinie .../.../EG [über den Universaldienst und Nutzerrechte bei elektronischen Kommunikationsnetzen und -diensten[ zu erschwinglichen Preisen Zugang zum Universaldienst erhalten;"

---

# legal_t5_small_trans_de_it_small_finetuned model

Model on translating legal text from Deustch to Italian. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three parallel corpus from jrc-acquis, europarl and dcep.

## Model description

legal_t5_small_trans_de_it_small_finetuned is initially pretrained on unsupervised task with the all of the data of the training set. The unsupervised task was "masked language modelling". legal_t5_small_trans_de_it_small_finetuned is based on the `t5-small` model and was trained on a large corpus of parallel text. This is a smaller model, which scales the baseline model of t5 down by using `dmodel = 512`, `dff = 2,048`, 8-headed attention, and only 6 layers each in the encoder and decoder. This variant has about 60 million parameters.

## Intended uses & limitations

The model could be used for translation of legal texts from Deustch to Italian.

### How to use

Here is how to use this model to translate legal text from Deustch to Italian in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_de_it_small_finetuned"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_de_it", do_lower_case=False,
                                            skip_special_tokens=True),
    device=0
)

de_text = "sicherstellen, dass alle Bürger gemäß der Richtlinie .../.../EG [über den Universaldienst und Nutzerrechte bei elektronischen Kommunikationsnetzen und -diensten[ zu erschwinglichen Preisen Zugang zum Universaldienst erhalten;"

pipeline([de_text], max_length=512)
```

## Training data

The legal_t5_small_trans_de_it_small_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 8 Million parallel texts.

## Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

### Pretraining

The pre-training data was the combined data from all the 42 language pairs. The task for the model was to predict the portions of a sentence which were masked randomly.

## Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_trans_de_it_small_finetuned | 42.895|

### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)


---
language: Swedish Spanish
tags:
- translation Swedish Spanish  model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "med beaktande av sin resolution av den 14 april 2005 om torkan i Portugal,"

---

# legal_t5_small_multitask_sv_es model

Model on translating legal text from Swedish to Spanish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). The model is parallely trained on the three parallel corpus with 42 language pair
from jrc-acquis, europarl and dcep along with the unsupervised task where the model followed the task of prediction in a masked language model.

## Model description

No pretraining is involved in case of legal_t5_small_multitask_sv_es model, rather the unsupervised task is added with all the translation task
to realize the multitask learning scenario.

## Intended uses & limitations

The model could be used for translation of legal texts from Swedish to Spanish.

### How to use

Here is how to use this model to translate legal text from Swedish to Spanish in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_multitask_sv_es"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_multitask_sv_es", do_lower_case=False,
                                            skip_special_tokens=True),
    device=0
)

sv_text = "med beaktande av sin resolution av den 14 april 2005 om torkan i Portugal,"

pipeline([sv_text], max_length=512)
```

## Training data

The legal_t5_small_multitask_sv_es model (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 8 Million parallel texts.

## Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule.

### Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

### Pretraining

## Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_multitask_sv_es | 35.506|

### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)


---
language: Spanish Deustch
tags:
- translation Spanish Deustch  model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "Estudios y publicaciones realizados por el Parlamento Europeo"

---

# legal_t5_small_multitask_es_de model

Model on translating legal text from Spanish to Deustch. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). The model is parallely trained on the three parallel corpus with 42 language pair
from jrc-acquis, europarl and dcep along with the unsupervised task where the model followed the task of prediction in a masked language model.

## Model description

No pretraining is involved in case of legal_t5_small_multitask_es_de model, rather the unsupervised task is added with all the translation task
to realize the multitask learning scenario.

## Intended uses & limitations

The model could be used for translation of legal texts from Spanish to Deustch.

### How to use

Here is how to use this model to translate legal text from Spanish to Deustch in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_multitask_es_de"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_multitask_es_de", do_lower_case=False,
                                            skip_special_tokens=True),
    device=0
)

es_text = "Estudios y publicaciones realizados por el Parlamento Europeo"

pipeline([es_text], max_length=512)
```

## Training data

The legal_t5_small_multitask_es_de model (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 8 Million parallel texts.

## Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule.

### Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

### Pretraining

## Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_multitask_es_de | 41.196|

### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)


---
language: Italian Deustch
tags:
- translation Italian Deustch  model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "di Alyn Smith (Verts/ALE)"

---

# legal_t5_small_multitask_it_de model

Model on translating legal text from Italian to Deustch. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). The model is parallely trained on the three parallel corpus with 42 language pair
from jrc-acquis, europarl and dcep along with the unsupervised task where the model followed the task of prediction in a masked language model.

## Model description

No pretraining is involved in case of legal_t5_small_multitask_it_de model, rather the unsupervised task is added with all the translation task
to realize the multitask learning scenario.

## Intended uses & limitations

The model could be used for translation of legal texts from Italian to Deustch.

### How to use

Here is how to use this model to translate legal text from Italian to Deustch in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_multitask_it_de"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_multitask_it_de", do_lower_case=False,
                                            skip_special_tokens=True),
    device=0
)

it_text = "di Alyn Smith (Verts/ALE)"

pipeline([it_text], max_length=512)
```

## Training data

The legal_t5_small_multitask_it_de model (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 8 Million parallel texts.

## Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule.

### Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

### Pretraining

## Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_multitask_it_de | 35.365|

### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)


---
tags:
- summarization
widget:
- text: "new file mode 100644 index 000000000 . . 892fda21b Binary files / dev / null and b / src / plugins / gateway / lib / joscar . jar differ"

---

# CodeTrans model for git commit message generation
Pretrained model on git commit using the t5 small model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized git commit: it works best with tokenized git commit.

## Model description

This CodeTrans model is based on the `t5-small` model. It has its own SentencePiece vocabulary model. It used transfer-learning pre-training on 7 unsupervised datasets in the software development domain. It is then fine-tuned on the git commit message generation task for the java commit changes.

## Intended uses & limitations

The model could be used to generate the git commit message for the git commit changes or be fine-tuned on other relevant tasks. It can be used on unparsed and untokenized commit changes. However, if the change is tokenized, the performance should be better.

### How to use

Here is how to use this model to generate git commit message using Transformers SummarizationPipeline:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_commit_generation_transfer_learning_finetune"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_commit_generation_transfer_learning_finetune", skip_special_tokens=True),
    device=0
)

tokenized_code = "new file mode 100644 index 000000000 . . 892fda21b Binary files / dev / null and b / src / plugins / gateway / lib / joscar . jar differ"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/transfer%20learning%20fine-tuning/commit%20generation/small_model.ipynb).
## Training data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)

## Training procedure

### Transfer-learning Pretraining

The model was trained on a single TPU Pod V3-8 for 500,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Fine-tuning

This model was then fine-tuned on a single TPU Pod V2-8 for 10,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing commit changes.

## Evaluation results

For the git commit message generation task, different models achieves the following results on different programming languages (in BLEU score):

Test results :

|   Language / Model   |      Java      |
| -------------------- | :------------: |
|   CodeTrans-ST-Small    |     39.61      |
|   CodeTrans-ST-Base     |     38.67      |
|   CodeTrans-TF-Small    |     44.22      |
|   CodeTrans-TF-Base     |     44.17      |
|   CodeTrans-TF-Large    |   **44.41**    |
|   CodeTrans-MT-Small    |     36.17      |
|   CodeTrans-MT-Base     |     39.25      |
|   CodeTrans-MT-Large    |     41.18      |
|   CodeTrans-MT-TF-Small |     43.96      |
|   CodeTrans-MT-TF-Base  |     44.19      |
|   CodeTrans-MT-TF-Large |     44.34      |
|   State of the art   |     32.81      |

> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)


---
language: Deustch Cszech
tags:
- translation Deustch Cszech  model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "Der Rahmenbeschluss sieht ein beschleunigtes Verfahren für die Anerkennung und Vollstreckung von freiheitsentziehenden Maßnahmen oder Maßnahmen der Sicherung (bei Unzurechnungsfähigkeit oder verminderter Schuldfähigkeit), die von einem Gericht eines anderen Mitgliedstaats gegen eine Person verhängt wurden, durch einen Mitgliedstaat vor, dessen Staatsangehörigkeit die Person besitzt, in dem sie ihren rechtmäßigen Aufenthalt hat oder zu dem sie enge Verbindungen hat."

---

# legal_t5_small_trans_de_cs_small_finetuned model

Model on translating legal text from Deustch to Cszech. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three parallel corpus from jrc-acquis, europarl and dcep.

## Model description

legal_t5_small_trans_de_cs_small_finetuned is initially pretrained on unsupervised task with the all of the data of the training set. The unsupervised task was "masked language modelling". legal_t5_small_trans_de_cs_small_finetuned is based on the `t5-small` model and was trained on a large corpus of parallel text. This is a smaller model, which scales the baseline model of t5 down by using `dmodel = 512`, `dff = 2,048`, 8-headed attention, and only 6 layers each in the encoder and decoder. This variant has about 60 million parameters.

## Intended uses & limitations

The model could be used for translation of legal texts from Deustch to Cszech.

### How to use

Here is how to use this model to translate legal text from Deustch to Cszech in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_de_cs_small_finetuned"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_de_cs", do_lower_case=False,
                                            skip_special_tokens=True),
    device=0
)

de_text = "Der Rahmenbeschluss sieht ein beschleunigtes Verfahren für die Anerkennung und Vollstreckung von freiheitsentziehenden Maßnahmen oder Maßnahmen der Sicherung (bei Unzurechnungsfähigkeit oder verminderter Schuldfähigkeit), die von einem Gericht eines anderen Mitgliedstaats gegen eine Person verhängt wurden, durch einen Mitgliedstaat vor, dessen Staatsangehörigkeit die Person besitzt, in dem sie ihren rechtmäßigen Aufenthalt hat oder zu dem sie enge Verbindungen hat."

pipeline([de_text], max_length=512)
```

## Training data

The legal_t5_small_trans_de_cs_small_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/dcep) dataset consisting of 5 Million parallel texts.

## Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

### Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

### Pretraining

The pre-training data was the combined data from all the 42 language pairs. The task for the model was to predict the portions of a sentence which were masked randomly.

## Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_trans_de_cs_small_finetuned | 43.750|

### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)