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barto_emergency_multi_purpose

Text2Text Generation
Transformers
Safetensors
Spanish
bart
Generated from Trainer
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metadata
license: other
base_model: vgaraujov/bart-base-spanish
tags:
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: barto_prompts
    results: []
language:
  - es
library_name: transformers
pipeline_tag: text2text-generation
widget:
  - text: >-
      Resume la emergencia: Buenos días estoy en el transporte público de mi
      ciudad, a una pasajera le están dando ataques de epilepsia, el señor
      conductor dice que estamos en el sector 42. La pasajera tiene
      aproximadamente 33 años por favor ayúdenos.
    example_title: Text summarization
  - text: >-
      Extrae las palabras clave de la emergencia: Buenas tardes, estoy viendo un
      incendio forestal enorme en el sector 12, envíen ayuda por favor.
    example_title: Keyword extraction
  - text: >-
      La palabra que mejor representa la emergencia es: Buenas noches, le acaban
      de robar el celular a mi amigo, envíen a la policía por favor.
    example_title: Word representation
  - text: >-
      Clasifica la emergencia en [CLAVE ROJA, CLAVE NARANJA, CLAVE AMARILLA,
      CLAVE VERDE]: Buenos días, me podrían ayudar por favor un vehículo se
      encuentra obstaculizando mi estacionamiento.
    example_title: Text classification

barto_prompts

This model is a fine-tuned version of vgaraujov/bart-base-spanish. It achieves the following results on the evaluation set:

  • Loss: 0.5242
  • Rouge1: 77.7794
  • Rouge2: 62.5213
  • Rougel: 77.3853
  • Rougelsum: 77.2245
  • Gen Len: 11.6686

Model description

This checkpoint uses BARTO as base model and different prefix to achieve different tasks in emergency transcribed calls:

  • "Resume la emergencia: ": For text summarization
  • "Extrae las palabras clave de la emergencia: ": For keyword extraction
  • "La palabra que mejor representa la emergencia es: ": Gives a word that represents the text
  • "Clasifica la emergencia en [CLAVE ROJA, CLAVE NARANJA, CLAVE AMARILLA, CLAVE VERDE]: ": For text classification

Intended uses & limitations

Under privacy agreement.

Training and evaluation data

Training data used has been provided by the ECU 911 service under a strict confidentiality agreement.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 3
  • total_train_batch_size: 48
  • total_eval_batch_size: 48
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
1.3631 1.0 92 0.6643 66.5661 49.8557 66.156 66.0723 10.7803
0.607 2.0 184 0.5528 72.4516 55.3295 72.0424 71.9591 10.8390
0.4994 3.0 276 0.5330 74.2798 56.9793 73.6683 73.6271 10.9072
0.4215 4.0 368 0.5246 75.5697 58.5086 75.1434 75.0331 11.5663
0.3744 5.0 460 0.5302 75.9054 60.4386 75.4245 75.294 11.6496
0.3392 6.0 552 0.5238 76.8758 61.7901 76.4882 76.444 11.7254
0.3014 7.0 644 0.5302 76.8835 61.9104 76.4603 76.3661 11.6117
0.2807 8.0 736 0.5239 77.4479 62.0839 77.0472 76.8683 11.5417
0.265 9.0 828 0.5210 77.5274 62.249 77.1446 76.9984 11.5890
0.2594 10.0 920 0.5242 77.7794 62.5213 77.3853 77.2245 11.6686

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0