Fine-tune an extractive Question Answer model on our own dataset

## I want to do this:

I want to fine-tune an extractive QA model on a CodeYourFuture dataset pulled from our docs and our Slack conversations.

## Here’s why I want to do it:

I want to provide a SMALL hosted model (probably an autotrain on our Huggingface org to make it as approachable as possible) that trainees can build interfaces to interact with
I want to provide an exciting but _achievable_ final project for trainees
I want to create a "taster" of AI and ML that is relevant to our course and illuminating for trainees, who have expressed a lot of interest in this emerging field
I want to reduce the burden on staff of constantly answering the same 5 questions over and over on Slack

## Here’s how it serves our goals:

All our trainees need lots more practice asking good questions and evaluating the answers; projects that create spaces for dialogic pedagogy are needed
_We Believe in Collective Intelligence_
An exciting final project should improve performance in hiring: _good jobs in tech_

## This is how much time I can put towards this change:

I have [stubbed a dataset](https://docs.google.com/spreadsheets/d/153_f9WHn3KN6X5ok_M2eUMEKMoYYtRAys_qYPnrLZJ0/edit?usp=sharing) to think about this more. I can spend up to 6 1 hour sessions on this
I have reached out to some ML/AI experts I know to get advice. I will spend up to 4 2 hours sessions getting advice
I have created an organisation on [Huggingface](https://huggingface.co/CodeYourFuture) -- please join! https://huggingface.co/CodeYourFuture
I have drafted (SUPER DRAFTY) an e[xample final project](https://docs.google.com/document/d/1Oc_1eoNS0ZIRNpAoaBMjBaj70pJoPo2Edj6WNfcqFOE/edit?usp=sharing) that interacts with this hypothetical model

## This is the help I need from others to get this done (if any):

- ML devs to evaluate and instruct on the preparation of data
- trainees and interns to prepare data (approx 3 hours each)
- Any interested parties to build small proof of concepts to share
- Possibly a sponsor to fund training cycles if we find they are needed

Comienza con la hoja de cálculo del conjunto de datos provisional, la organización de CodeYourFuture en Hugging Face y el borrador del documento del proyecto final. Primero determina los requisitos de preparación de datos y entrenamiento del modelo con los expertos en ML mencionados; la tarea estaría terminada cuando haya un conjunto de datos acordado, un modelo de QA extractivo entrenado y alojado, y una prueba de concepto para que la utilicen los trainees.

Escrito por el modelo de indexación a partir del texto del issue.