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lora-roberta-large-0927
This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.5366
- Accuracy: 0.4472
- Prec: 0.2000
- Recall: 0.4472
- F1: 0.2763
- B Acc: 0.1429
- Micro F1: 0.4472
- Prec Joy: 0.0
- Recall Joy: 0.0
- F1 Joy: 0.0
- Prec Anger: 0.0
- Recall Anger: 0.0
- F1 Anger: 0.0
- Prec Disgust: 0.0
- Recall Disgust: 0.0
- F1 Disgust: 0.0
- Prec Fear: 0.0
- Recall Fear: 0.0
- F1 Fear: 0.0
- Prec Neutral: 0.4472
- Recall Neutral: 1.0
- F1 Neutral: 0.6180
- Prec Sadness: 0.0
- Recall Sadness: 0.0
- F1 Sadness: 0.0
- Prec Surprise: 0.0
- Recall Surprise: 0.0
- F1 Surprise: 0.0
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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 25.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Prec | Recall | F1 | B Acc | Micro F1 | Prec Joy | Recall Joy | F1 Joy | Prec Anger | Recall Anger | F1 Anger | Prec Disgust | Recall Disgust | F1 Disgust | Prec Fear | Recall Fear | F1 Fear | Prec Neutral | Recall Neutral | F1 Neutral | Prec Sadness | Recall Sadness | F1 Sadness | Prec Surprise | Recall Surprise | F1 Surprise |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.8381 | 1.25 | 2092 | 1.5415 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4866 | 2.5 | 4184 | 1.5564 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4862 | 3.75 | 6276 | 1.5700 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4762 | 5.0 | 8368 | 1.5391 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4765 | 6.25 | 10460 | 1.5566 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4848 | 7.5 | 12552 | 1.5411 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4782 | 8.75 | 14644 | 1.5548 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4943 | 10.0 | 16736 | 1.6115 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4801 | 11.25 | 18828 | 1.5424 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4946 | 12.5 | 20920 | 1.5637 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4867 | 13.75 | 23012 | 1.5492 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4957 | 15.01 | 25104 | 1.5812 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4913 | 16.26 | 27196 | 1.5425 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.5007 | 17.51 | 29288 | 1.5446 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4919 | 18.76 | 31380 | 1.5616 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4895 | 20.01 | 33472 | 1.5502 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4946 | 21.26 | 35564 | 1.5398 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4754 | 22.51 | 37656 | 1.5307 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4824 | 23.76 | 39748 | 1.5356 | 0.4472 | 0.2000 | 0.4472 | 0.2763 | 0.1429 | 0.4472 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 1.0 | 0.6180 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
