readme: ---
  language:
  - en
  - es
  - multilingual
  tags:
  - translation
  datasets:
  - ted_talks_iwslt
  metrics:
  - rouge
  ---
  
  Subtitle Translation Model
  ===============
  
  This is a model for text translation between Spanish and English texts. It has been trained with Spanish and English Ted Talks transcriptions from [ted_talks_iwslt](https://huggingface.co/datasets/ted_talks_iwslt), finetuning the [Helsinki-NLP/opus-mt-en-mul](https://huggingface.co/Helsinki-NLP/opus-mt-en-mul) model.
  
  
  #### Intended Use
  
  This model has been trained with the intention of building a tool for subtitle translation.
  
  #### Data
  
  The dataset has been split into the following structure:
  
  ```python
  DatasetDict({
      train: Dataset({
          features: ['Original_Sentence', 'Translate_SP', '__index_level_0__'],
          num_rows: 2454
      })
      validation: Dataset({
          features: ['Original_Sentence', 'Translate_SP', '__index_level_0__'],
          num_rows: 307
      })
      test: Dataset({
          features: ['Original_Sentence', 'Translate_SP', '__index_level_0__'],
          num_rows: 307
      })
  })
  ```
  Note: Evaluation numbers have been obtained using 50 samples from test set.
  
  #### Relevant Training Arguments
  ```python
      evaluation_strategy = "epoch"
      learning_rate=2e-5
      per_device_train_batch_size=4
      per_device_eval_batch_size=4
      weight_decay=0.01
      save_total_limit=3
      num_train_epochs=1
      predict_with_generate=True
      fp16=False
  ```
  
  #### Evaluation Results
  
  The following results show the rouge metrics obtained during the training process (evaluation of the hiperparameters) and the evaluation of the model itself with the test set.
  
  - Eval metrics 
  ```python
  {'rouge1': 64.95, 'rouge2': 42.24, 'rougeL': 61.97, 'rougeLsum': 62.93}
  ```
  - Test set evaluation (50 transcriptions)
  ```python
  {'rouge1': 65.54,'rouge2': 41.45,'rougeL': 62.72,'rougeLsum': 62.83}
  ```
  #### Using the model
  
  This model can be easily used with the following lines of code:
  
  ```python
  from transformers import pipeline
  pipe = pipeline(model="razwand/opus-mt-en-mul-finetuned_en_sp_translator")
  pipe("Hi everyone!")
  
  >>[{'translation_text': 'Hola a todos!'}]
  
  ```
  
  
  
  