| | --- |
| | 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 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # 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 |
