File size: 6,010 Bytes
15f33eb 153b7a0 15f33eb 153b7a0 15f33eb 153b7a0 15f33eb 153b7a0 15f33eb |
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 |
---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
model-index:
- name: fine_tuned_roberta
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. -->
# fine_tuned_roberta
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3144
- F1: 0.44
- F5: 0.5013
- Precision: 0.3333
- Recall: 0.6471
## 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: 256
- eval_batch_size: 256
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | F5 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|:------:|
| No log | 1.0 | 9 | 0.3437 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 18 | 0.3128 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 3.0 | 27 | 0.3089 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 4.0 | 36 | 0.3058 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 5.0 | 45 | 0.2960 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 6.0 | 54 | 0.2744 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 7.0 | 63 | 0.2674 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 8.0 | 72 | 0.2744 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 9.0 | 81 | 0.2745 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 10.0 | 90 | 0.2849 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 11.0 | 99 | 0.3079 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 12.0 | 108 | 0.2686 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 13.0 | 117 | 0.2856 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 14.0 | 126 | 0.3047 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 15.0 | 135 | 0.2697 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 16.0 | 144 | 0.2783 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 17.0 | 153 | 0.2816 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 18.0 | 162 | 0.2660 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 19.0 | 171 | 0.3168 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 20.0 | 180 | 0.2796 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 21.0 | 189 | 0.2956 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 22.0 | 198 | 0.2610 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 23.0 | 207 | 0.2680 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 24.0 | 216 | 0.2836 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 25.0 | 225 | 0.2841 | 0.0588 | 0.0499 | 0.1111 | 0.04 |
| No log | 26.0 | 234 | 0.3023 | 0.2128 | 0.2077 | 0.2273 | 0.2 |
| No log | 27.0 | 243 | 0.3097 | 0.2903 | 0.3135 | 0.2432 | 0.36 |
| No log | 28.0 | 252 | 0.3118 | 0.2963 | 0.3049 | 0.2759 | 0.32 |
| No log | 29.0 | 261 | 0.3110 | 0.3256 | 0.3065 | 0.3889 | 0.28 |
| No log | 30.0 | 270 | 0.3261 | 0.2927 | 0.2700 | 0.375 | 0.24 |
| No log | 31.0 | 279 | 0.3167 | 0.4 | 0.4144 | 0.3667 | 0.44 |
| No log | 32.0 | 288 | 0.2997 | 0.2857 | 0.2663 | 0.3529 | 0.24 |
| No log | 33.0 | 297 | 0.3202 | 0.3385 | 0.3712 | 0.275 | 0.44 |
| No log | 34.0 | 306 | 0.2841 | 0.3922 | 0.3951 | 0.3846 | 0.4 |
| No log | 35.0 | 315 | 0.3986 | 0.4048 | 0.4788 | 0.2881 | 0.68 |
| No log | 36.0 | 324 | 0.2881 | 0.4082 | 0.4050 | 0.4167 | 0.4 |
| No log | 37.0 | 333 | 0.2719 | 0.3500 | 0.3195 | 0.4667 | 0.28 |
| No log | 38.0 | 342 | 0.3239 | 0.4615 | 0.5062 | 0.375 | 0.6 |
| No log | 39.0 | 351 | 0.2652 | 0.4906 | 0.5014 | 0.4643 | 0.52 |
| No log | 40.0 | 360 | 0.2813 | 0.5 | 0.5213 | 0.4516 | 0.56 |
| No log | 41.0 | 369 | 0.3664 | 0.4507 | 0.5081 | 0.3478 | 0.64 |
| No log | 42.0 | 378 | 0.2577 | 0.4651 | 0.4379 | 0.5556 | 0.4 |
| No log | 43.0 | 387 | 0.3193 | 0.4928 | 0.5507 | 0.3864 | 0.68 |
| No log | 44.0 | 396 | 0.2627 | 0.4615 | 0.4684 | 0.4444 | 0.48 |
| No log | 45.0 | 405 | 0.2850 | 0.4643 | 0.4841 | 0.4194 | 0.52 |
| No log | 46.0 | 414 | 0.2971 | 0.5574 | 0.5986 | 0.4722 | 0.68 |
| No log | 47.0 | 423 | 0.2804 | 0.5185 | 0.5336 | 0.4828 | 0.56 |
| No log | 48.0 | 432 | 0.2845 | 0.5091 | 0.5274 | 0.4667 | 0.56 |
| No log | 49.0 | 441 | 0.3005 | 0.5312 | 0.5797 | 0.4359 | 0.68 |
| No log | 50.0 | 450 | 0.2981 | 0.5079 | 0.5514 | 0.4211 | 0.64 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0a0+ebedce2
- Datasets 2.17.1
- Tokenizers 0.15.2
|