Update README.md

README.md

@@ -1,32 +1,20 @@
 ---
-license:  apache-2.0
+license: apache-2.0
+datasets:
+- projecte-aina/CA-IT_Parallel_Corpus
+language:
+- it
+- ca
+metrics:
+- bleu
+library_name: fairseq
 ---
 ## Projecte Aina’s Italian-Catalan machine translation model
-
-## Table of Contents
-- [Model Description](#model-description)
-- [Intended Uses and Limitations](#intended-use)
-- [How to Use](#how-to-use)
-- [Training](#training)
-  - [Training data](#training-data)
-  - [Training procedure](#training-procedure)
-	- [Data Preparation](#data-preparation)
-	- [Tokenization](#tokenization)
-	- [Hyperparameters](#hyperparameters)
-- [Evaluation](#evaluation)
-   - [Variable and Metrics](#variable-and-metrics)
-   - [Evaluation Results](#evaluation-results)
-- [Additional Information](#additional-information)
-  - [Author](#author)
-  - [Contact Information](#contact-information)
-  - [Copyright](#copyright)
-  - [Licensing Information](#licensing-information)
-  - [Funding](#funding)
-  - [Disclaimer](#disclaimer)
  
 ## Model description
 
-This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Catalan-Italian datasets, which after filtering and cleaning comprised 9.482.927 sentence pairs. The model was evaluated on the Flores and NTREX evaluation datasets. 
+This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Catalan-Italian datasets, 
+which after filtering and cleaning comprised 9.482.927 sentence pairs. The model was evaluated on the Flores and NTREX evaluation datasets. 
 
 ## Intended uses and limitations
 
@@ -46,7 +34,7 @@ Translate a sentence using python
 import ctranslate2
 import pyonmttok
 from huggingface_hub import snapshot_download
-model_dir = snapshot_download(repo_id="projecte-aina/mt-aina-it-ca", revision="main")
+model_dir = snapshot_download(repo_id="projecte-aina/aina-translator-it-ca", revision="main")
 tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model")
 tokenized=tokenizer.tokenize("Benvenuto al progetto Aina!")
 translator = ctranslate2.Translator(model_dir)
@@ -77,12 +65,16 @@ The model was trained on a combination of the following datasets:
 
 ### Data preparation
 
- All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75. This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE). The filtered datasets are then concatenated to form a final corpus of 9.482.927 and before training the punctuation is normalized using a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py)
+ All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75. 
+ This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE). 
+ The filtered datasets are then concatenated to form a final corpus of 9.482.927 and before training the punctuation is normalized using 
+ a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py)
 
 
 #### Tokenization
 
- All data is tokenized using sentencepiece, with a 50 thousand token sentencepiece model  learned from the combination of all filtered training data. This model is included.  
+ All data is tokenized using sentencepiece, with a 50 thousand token sentencepiece model learned from the combination of all filtered training data. 
+ This model is included.  
 
 #### Hyperparameters
 
@@ -116,13 +108,14 @@ The model was trained for a total of 19.000 updates. Weights were saved every 10
 
 ### Variable and metrics
 
-We use the BLEU score for evaluation on the [Flores-101](https://github.com/facebookresearch/flores), and [NTREX](https://github.com/MicrosoftTranslator/NTREX) evaluation datasets. 
+We use the BLEU score for evaluation on the [Flores-101](https://github.com/facebookresearch/flores) and [NTREX](https://github.com/MicrosoftTranslator/NTREX) test sets.
 
 ### Evaluation results
 
-Below are the evaluation results on the machine translation from Italian to Catalan compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es):
+Below are the evaluation results on the machine translation from Italian to Catalan compared to [Softcatalà](https://www.softcatala.org/) and 
+[Google Translate](https://translate.google.es/?hl=es):
 
-| Test set         	| SoftCatalà | Google Translate |mt-aina-it-ca|
+| Test set         	| SoftCatalà | Google Translate | aina-translator-it-ca |
 |----------------------|------------|------------------|---------------|
 | Flores 101 dev   	| 25,4     	| **30,4**     	| 27,5     	|
 | Flores 101 devtest   |26,6   	| **31,2**     	| 27,7     	|
@@ -132,30 +125,34 @@ Below are the evaluation results on the machine translation from Italian to Cata
 ## Additional information
 
 ### Author
-Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center.
+The Language Technologies Unit from Barcelona Supercomputing Center.
 
-### Contact information
-For further information, send an email to <langtech@bsc.es>
+### Contact
+For further information, please send an email to <langtech@bsc.es>.
 
 ### Copyright
-Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023)
+Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.
 
-### Licensing information
-This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+### License
+[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
 
 ### Funding
-This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project] (https://projecteaina.cat/).
-
-## Limitations and Bias
-At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
+This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/).
 
 ### Disclaimer
 
 <details>
 <summary>Click to expand</summary>
-The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
-When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
-In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
-</details>
 
+The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0. 
+
+Be aware that the model may have biases and/or any other undesirable distortions.
+
+When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it) 
+or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, 
+in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
+
+In no event shall the owner and creator of the model (Barcelona Supercomputing Center) 
+be liable for any results arising from the use made by third parties.
 
+</details>