Emergency Text to Text Checkpoints
Collection
3 items • Updated
YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
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
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Model tree for UDA-LIDI/barto_emergency_multi_purpose
Base model
vgaraujov/bart-base-spanish