File size: 4,623 Bytes
c91574e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: L_Roberta3
results: []
---
<!-- 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. -->
# L_Roberta3
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2095
- Accuracy: 0.9555
- F1: 0.9555
- Precision: 0.9555
- Recall: 0.9555
- C Report: precision recall f1-score support
0 0.97 0.95 0.96 876
1 0.94 0.97 0.95 696
accuracy 0.96 1572
macro avg 0.95 0.96 0.96 1572
weighted avg 0.96 0.96 0.96 1572
- C Matrix: None
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | C Report | C Matrix |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
| 0.2674 | 1.0 | 329 | 0.2436 | 0.9389 | 0.9389 | 0.9389 | 0.9389 | precision recall f1-score support
0 0.94 0.95 0.95 876
1 0.94 0.92 0.93 696
accuracy 0.94 1572
macro avg 0.94 0.94 0.94 1572
weighted avg 0.94 0.94 0.94 1572
| None |
| 0.1377 | 2.0 | 658 | 0.1506 | 0.9408 | 0.9408 | 0.9408 | 0.9408 | precision recall f1-score support
0 0.97 0.92 0.95 876
1 0.91 0.96 0.94 696
accuracy 0.94 1572
macro avg 0.94 0.94 0.94 1572
weighted avg 0.94 0.94 0.94 1572
| None |
| 0.0898 | 3.0 | 987 | 0.1491 | 0.9548 | 0.9548 | 0.9548 | 0.9548 | precision recall f1-score support
0 0.96 0.96 0.96 876
1 0.95 0.95 0.95 696
accuracy 0.95 1572
macro avg 0.95 0.95 0.95 1572
weighted avg 0.95 0.95 0.95 1572
| None |
| 0.0543 | 4.0 | 1316 | 0.1831 | 0.9561 | 0.9561 | 0.9561 | 0.9561 | precision recall f1-score support
0 0.97 0.95 0.96 876
1 0.94 0.96 0.95 696
accuracy 0.96 1572
macro avg 0.95 0.96 0.96 1572
weighted avg 0.96 0.96 0.96 1572
| None |
| 0.0394 | 5.0 | 1645 | 0.2095 | 0.9555 | 0.9555 | 0.9555 | 0.9555 | precision recall f1-score support
0 0.97 0.95 0.96 876
1 0.94 0.97 0.95 696
accuracy 0.96 1572
macro avg 0.95 0.96 0.96 1572
weighted avg 0.96 0.96 0.96 1572
| None |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|