bert2bert spanish question generation

Introduction
The BERT2BERT-Spanish-Question-Generation model is a text-to-text generation model fine-tuned for generating questions in Spanish. It is built using the Transformers library and is designed to work with PyTorch. The model is optimized for Spanish language processing and utilizes an encoder-decoder architecture.

Architecture
This model employs an encoder-decoder architecture, which is commonly used in text generation tasks. The model is based on the BERT2BERT architecture, ensuring effective processing and generation of Spanish text, particularly focused on question generation.

Training
The model has been fine-tuned on the SQuAD (Spanish) dataset. This fine-tuning process helps the model to generate contextually relevant and grammatically correct questions in Spanish. The training process leverages the capabilities of pre-trained BERT models, adapting them specifically for the task of question generation.

Guide: Running Locally

1. Install Dependencies: Ensure that you have Python and PyTorch installed. Use pip to install the Hugging Face Transformers library.

   pip install transformers
   

2. Load the Model: Utilize the Transformers library to load the model.

   from transformers import BertTokenizer, EncoderDecoderModel
   
   model = EncoderDecoderModel.from_pretrained("mrm8488/bert2bert-spanish-question-generation")
   tokenizer = BertTokenizer.from_pretrained("mrm8488/bert2bert-spanish-question-generation")
   

3. Generate Questions: Prepare your input data and use the model to generate questions.

   inputs = tokenizer("Manuel vive en Murcia, España", return_tensors="pt")
   outputs = model.generate(**inputs)
   question = tokenizer.decode(outputs[0], skip_special_tokens=True)
   print(question)
   

4. Suggest Cloud GPUs: For faster processing, consider using cloud-based GPU services such as AWS EC2, Google Cloud Platform, or Azure.

License
The model is available under an open license, which allows for use, modification, and distribution. Users should review the specific terms on the Hugging Face model card page to ensure compliance with any restrictions or obligations.