metadata
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: attribute_minig_mslacerda
results: []
attribute_minig_mslacerda
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5688
- Precision: 0.7424
- Recall: 0.7766
- F1: 0.7591
- Accuracy: 0.8673
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: 4.546321141328063e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 207
- num_epochs: 12
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 85 | 4.0840 | 0.0271 | 0.0229 | 0.0248 | 0.1523 |
| No log | 2.0 | 170 | 2.1874 | 0.3096 | 0.2348 | 0.2671 | 0.5099 |
| No log | 3.0 | 255 | 1.2897 | 0.4893 | 0.4684 | 0.4786 | 0.6940 |
| No log | 4.0 | 340 | 0.8647 | 0.6485 | 0.6413 | 0.6449 | 0.8027 |
| No log | 5.0 | 425 | 0.6897 | 0.6977 | 0.7051 | 0.7014 | 0.8414 |
| 1.93 | 6.0 | 510 | 0.6046 | 0.7177 | 0.7354 | 0.7264 | 0.8550 |
| 1.93 | 7.0 | 595 | 0.5833 | 0.7339 | 0.7602 | 0.7468 | 0.8628 |
| 1.93 | 8.0 | 680 | 0.5722 | 0.7474 | 0.7627 | 0.7550 | 0.8643 |
| 1.93 | 9.0 | 765 | 0.5704 | 0.7451 | 0.7695 | 0.7571 | 0.8694 |
| 1.93 | 10.0 | 850 | 0.5802 | 0.7485 | 0.7763 | 0.7622 | 0.8715 |
| 1.93 | 11.0 | 935 | 0.5723 | 0.7539 | 0.7838 | 0.7685 | 0.8754 |
| 0.1632 | 12.0 | 1020 | 0.5736 | 0.7497 | 0.7813 | 0.7652 | 0.8739 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1