Hugging Face: IA Colaborativa

En este repositorio estar谩 disponible el c贸digo y modelo que entren茅 para la charla "Hugging Face: IA Colaborativa" del FLISoL de C贸rdoba, Argentina, de 2023.

Para inicializar el setup hace falta tener instalado y activado git-lfs.

Pueden clonar el repositorio con:

$ git clone

Y luego crean el entorno e instalan los requerimientos.

$ python -m venv flisol-venv
$ source ./flisol-venv/bin/activate
(flisol-venv) $ pip install -r requirements.txt

El c贸digo est谩 probado con Python 3.10, pero deber铆a funcionar con Python >= 3.8. En los requerimientos est谩 organizado para instalar PyTorch v2.0.0 para cpu, pero pueden ajustarlo para utilizar GPUs suponiendo que cumplan los requerimientos de CUDA.


Copyright (C) 2023 Cristian Cardellino

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <>.

Model Specifications (Auto Generated)

This model is a fine-tuned version of DeepESP/gpt2-spanish on the ./data/martin-fierro_train.txt dataset. It achieves the following results on the evaluation set:

  • Loss: 3.9067

Model description

GPT-2 model finetuned on the poem "El Gaucho Martin Fierro"

Intended uses & limitations

This was trained for the talk "Hugging Face: IA Colaborativa" @ FLISoL de C贸rdoba, Argentina, 2023.

Training and evaluation data

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss
4.3864 1.0 18 4.2025
3.948 2.0 36 4.0440
3.7962 3.0 54 3.9804
3.6105 4.0 72 3.9458
3.4444 5.0 90 3.9280
3.3855 6.0 108 3.9192
3.3142 7.0 126 3.9091
3.2192 8.0 144 3.9074
3.1615 9.0 162 3.9070
3.1637 10.0 180 3.9067

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.0+cpu
  • Datasets 2.11.0
  • Tokenizers 0.13.3
Downloads last month
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.