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Prototipo_3_EMI
Armandodelca/Prototipo_3_EMI
Prototipo_3_EMI is a text classification model from Armandodelca. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- distilbert
- Access
- Public
- Created
- May 3, 2024
- Updated
- May 3, 2024
- SHA
- 1eba8dde
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Base models
Model card
From the Hugging Face model README
<!-- 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. -->
Prototipo_3_EMI
This model is a fine-tuned version of dccuchile/distilbert-base-spanish-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2540
- Accuracy: 0.5423
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.2186 | 0.1778 | 200 | 1.1556 | 0.4803 |
| 1.1345 | 0.3556 | 400 | 1.0663 | 0.525 |
| 1.102 | 0.5333 | 600 | 1.0479 | 0.5293 |
| 1.1325 | 0.7111 | 800 | 1.0483 | 0.5353 |
| 1.1211 | 0.8889 | 1000 | 1.0337 | 0.521 |
| 0.9736 | 1.0667 | 1200 | 1.0006 | 0.5503 |
| 0.9428 | 1.2444 | 1400 | 1.0214 | 0.5523 |
| 0.9095 | 1.4222 | 1600 | 1.0174 | 0.555 |
| 0.9806 | 1.6 | 1800 | 1.0155 | 0.5527 |
| 0.969 | 1.7778 | 2000 | 1.0043 | 0.5547 |
| 0.9112 | 1.9556 | 2200 | 1.0050 | 0.5537 |
| 0.7557 | 2.1333 | 2400 | 1.0496 | 0.5607 |
| 0.8212 | 2.3111 | 2600 | 1.0494 | 0.5597 |
| 0.7695 | 2.4889 | 2800 | 1.0510 | 0.5687 |
| 0.7648 | 2.6667 | 3000 | 1.0513 | 0.5603 |
| 0.8232 | 2.8444 | 3200 | 1.0316 | 0.563 |
| 0.6288 | 3.0222 | 3400 | 1.0883 | 0.5503 |
| 0.6736 | 3.2 | 3600 | 1.1232 | 0.548 |
| 0.682 | 3.3778 | 3800 | 1.1695 | 0.543 |
| 0.6682 | 3.5556 | 4000 | 1.1608 | 0.5427 |
| 0.6516 | 3.7333 | 4200 | 1.1636 | 0.545 |
| 0.6731 | 3.9111 | 4400 | 1.1694 | 0.5403 |
| 0.5388 | 4.0889 | 4600 | 1.2120 | 0.544 |
| 0.5663 | 4.2667 | 4800 | 1.2278 | 0.544 |
| 0.5579 | 4.4444 | 5000 | 1.2439 | 0.538 |
| 0.5216 | 4.6222 | 5200 | 1.2507 | 0.5427 |
| 0.4634 | 4.8 | 5400 | 1.2531 | 0.5393 |
| 0.5359 | 4.9778 | 5600 | 1.2540 | 0.5423 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- Created
- May 3, 2024
- Updated
- May 3, 2024