readme: 
  # descripcion del modelo
  
  <!-- Provide a quick summary of what the model is/does. -->
  Modelo gpt-2_Neo125M Fine Tune, para la prediccion de precios de casas o apartamentos en Cali-Colombia [Descargue todos los archivos requeridos desde Dropbox](https://www.dropbox.com/scl/fo/4mj054l8eha31pcc5z245/h?dl=0&rlkey=q1s2pycl9hh65b3xvqiukh9g6).
  
  
  - **Developed by:** Nicolai Potes
  - **Language:** Python
  - **Finetuned from model :** [gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M).
  
  # Training Details
  ```
    Num examples = 779
    Num Epochs = 500
    Instantaneous batch size per device = 80
    Total train batch size (w. parallel, distributed & accumulation) = 80
    Gradient Accumulation steps = 1
    Total optimization steps = 5000
    Number of trainable parameters = 125200128
  ```
  
  # Training Evaluate
  ```
   {'eval_loss': 1.341125726699829,
   'eval_runtime': 23.3347,
   'eval_samples_per_second': 300.111,
   'eval_steps_per_second': 3.771,
   'epoch': 500.0}
  ```
  
  ## Training Data
  
  datos sacados de https://www.metrocuadrado.com/
  formato para el entrenamiento del mododelo
  ```
   'meter: 3651685 \n area: 267 \n bathroom: 4 \n room: 4 \n property: 1 \n price: 975000000',
   'meter: 3206498 \n area: 70 \n bathroom: 3 \n room: 4 \n property: 2 \n price: 225000000',
   'meter: 2181818 \n area: 110 \n bathroom: 2 \n room: 3 \n property: 2 \n price: 240000000',
   'meter: 5882352 \n area: 306 \n bathroom: 4 \n room: 4 \n property: 2 \n price: 1800000000',
   'meter: 2827586 \n area: 58 \n bathroom: 2 \n room: 2 \n property: 2 \n price: 164000000',
   'meter: 7382550 \n area: 149 \n bathroom: 4 \n room: 3 \n property: 2 \n price: 1100000000',
   'meter: 2833333 \n area: 300 \n bathroom: 3 \n room: 3 \n property: 1 \n price: 850000000',
   'meter: 3678474 \n area: 73 \n bathroom: 2 \n room: 3 \n property: 2 \n price: 270000000',
   'meter: 2254901 \n area: 51 \n bathroom: 2 \n room: 2 \n property: 2 \n price: 115000000',
   'meter: 2500000 \n area: 90 \n bathroom: 3 \n room: 3 \n property: 2 \n price: 225000000',
   'meter: 4508196 \n area: 122 \n bathroom: 5 \n room: 4 \n property: 2 \n price: 550000000',
   'meter: 3489583 \n area: 96 \n bathroom: 3 \n room: 3 \n property: 2 \n price: 335000000',
   'meter: 2151898 \n area: 395 \n bathroom: 5 \n room: 5 \n property: 1 \n price: 850000000',
  ```
  
  ### Hardware GPU
  ```
  +-----------------------------------------------------------------------------+
  | NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |
  |-------------------------------+----------------------+----------------------+
  | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
  | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
  |                               |                      |               MIG M. |
  |===============================+======================+======================|
  |   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
  | N/A   49C    P0    29W /  70W |      0MiB / 15360MiB |      5%      Default |
  |                               |                      |                  N/A |
  +-------------------------------+----------------------+----------------------+
                                                                                 
  +-----------------------------------------------------------------------------+
  | Processes:                                                                  |
  |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
  |        ID   ID                                                   Usage      |
  |=============================================================================|
  |  No running processes found                                                 |
  +-----------------------------------------------------------------------------+
  ```
  
  ### Librerias requeridas
  ```
  pip install transformers 
  pip install torch
  ```
  
  ### Codigo Python para cargar el modelo y predecir el valor de una propiedad (Casa/Apartamento)
  ```Python
  import pandas as pd
  import torch
  import transformers
  import re
  
  
  '''
  #dado el caso de estar en Google colab
  from google.colab import drive
  drive.mount('/content/drive')
  path="/content/drive/My Drive/DatosMetroCuadradoPrueba/"
  '''
  
  path="[Direccion de la carpeta donde tiene el MODELO]"
  
  path_carga= path+"modeloEntrenadoPreciosCasasApartamentos"
  
  from transformers import GPT2Tokenizer, GPTNeoForCausalLM
  new_modelPredict = GPTNeoForCausalLM.from_pretrained(path_carga).cuda()
  tokenizer2 = GPT2Tokenizer.from_pretrained(path_carga)
  new_modelPredict.resize_token_embeddings(len(tokenizer2))
  
  tipo_propiedad= 1 # 1: casa , 2:apartamento
  habitaciones= 5
  baños= 5
  area= 580 
  valor_inmueble= 1500000000
  valorMetroCuadrado= int(valor_inmueble/area)
  
  propiedad = f"<|startoftext|>meter: {valorMetroCuadrado} \n area: {area} \n bathroom: {baños} \n room: {habitaciones} \n property: {tipo_propiedad} \n price:"
  print("Texto:",propiedad)
  
  generated = tokenizer2(propiedad,    #  <|pad|>
      