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# barto_prompts

This model is a fine-tuned version of [vgaraujov/bart-base-spanish](https://huggingface.co/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
{"language": ["es"], "license": "other", "library_name": "transformers", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "vgaraujov/bart-base-spanish", "pipeline_tag": "text2text-generation", "widget": [{"text": "Resume la emergencia: Buenos d\u00edas estoy en el transporte p\u00fablico de mi ciudad, a una pasajera le est\u00e1n dando ataques de epilepsia, el se\u00f1or conductor dice que estamos en el sector 42. La pasajera tiene aproximadamente 33 a\u00f1os por favor ay\u00fadenos.", "example_title": "Text summarization"}, {"text": "Extrae las palabras clave de la emergencia: Buenas tardes, estoy viendo un incedio forestal enorme en el sector 12, env\u00eden 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, env\u00eden a la polic\u00eda por favor.", "example_title": "Word representation"}, {"text": "Clasifica la emergencia en [CLAVE ROJA, CLAVE NARANJA, CLAVE AMARILLA, CLAVE VERDE]: Buenos d\u00edas, me podr\u00edan ayudar por favor un veh\u00edculo se encuentra obstaculizando mi estacionamiento.", "example_title": "Text classification"}], "model-index": [{"name": "barto_prompts", "results": []}]}
transformers safetensors bart text2text-generation generated_from_trainer es base_model:vgaraujov/bart-base-spanish license:other autotrain_compatible endpoints_compatible region:us