File size: 6,835 Bytes
ba2a19f 1db8825 ba2a19f 1db8825 ba2a19f 1db8825 ba2a19f 1db8825 ba2a19f 1db8825 ba2a19f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: roberta-base-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9323394495412844
---
<!-- 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. -->
# roberta-base-sst2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1952
- Accuracy: 0.9323
## 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: 2e-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_ratio: 0.06
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.575 | 0.12 | 500 | 0.2665 | 0.9071 |
| 0.2989 | 0.24 | 1000 | 0.2088 | 0.9220 |
| 0.2725 | 0.36 | 1500 | 0.2560 | 0.9243 |
| 0.2814 | 0.48 | 2000 | 0.2016 | 0.9266 |
| 0.2586 | 0.59 | 2500 | 0.2293 | 0.9174 |
| 0.2536 | 0.71 | 3000 | 0.2340 | 0.9323 |
| 0.2494 | 0.83 | 3500 | 0.1952 | 0.9323 |
| 0.2396 | 0.95 | 4000 | 0.2494 | 0.9323 |
| 0.2123 | 1.07 | 4500 | 0.2187 | 0.9381 |
| 0.2042 | 1.19 | 5000 | 0.2812 | 0.9151 |
| 0.2083 | 1.31 | 5500 | 0.2739 | 0.9346 |
| 0.2041 | 1.43 | 6000 | 0.2087 | 0.9381 |
| 0.1969 | 1.54 | 6500 | 0.2590 | 0.9255 |
| 0.1982 | 1.66 | 7000 | 0.2445 | 0.9300 |
| 0.1943 | 1.78 | 7500 | 0.2798 | 0.9266 |
| 0.1848 | 1.9 | 8000 | 0.2844 | 0.9312 |
| 0.1788 | 2.02 | 8500 | 0.2998 | 0.9255 |
| 0.1623 | 2.14 | 9000 | 0.2696 | 0.9392 |
| 0.1499 | 2.26 | 9500 | 0.2533 | 0.9278 |
| 0.1426 | 2.38 | 10000 | 0.2971 | 0.9300 |
| 0.1479 | 2.49 | 10500 | 0.2596 | 0.9358 |
| 0.1405 | 2.61 | 11000 | 0.2945 | 0.9255 |
| 0.1577 | 2.73 | 11500 | 0.4061 | 0.9002 |
| 0.1521 | 2.85 | 12000 | 0.2724 | 0.9335 |
| 0.1426 | 2.97 | 12500 | 0.2712 | 0.9427 |
| 0.1206 | 3.09 | 13000 | 0.2954 | 0.9358 |
| 0.1074 | 3.21 | 13500 | 0.2653 | 0.9392 |
| 0.112 | 3.33 | 14000 | 0.2778 | 0.9346 |
| 0.1147 | 3.44 | 14500 | 0.3705 | 0.9312 |
| 0.1196 | 3.56 | 15000 | 0.2890 | 0.9346 |
| 0.1159 | 3.68 | 15500 | 0.3449 | 0.9266 |
| 0.119 | 3.8 | 16000 | 0.3207 | 0.9335 |
| 0.1268 | 3.92 | 16500 | 0.3235 | 0.9312 |
| 0.1074 | 4.04 | 17000 | 0.3650 | 0.9335 |
| 0.0805 | 4.16 | 17500 | 0.3338 | 0.9381 |
| 0.0838 | 4.28 | 18000 | 0.4302 | 0.9209 |
| 0.0848 | 4.39 | 18500 | 0.4096 | 0.9323 |
| 0.0922 | 4.51 | 19000 | 0.3332 | 0.9369 |
| 0.091 | 4.63 | 19500 | 0.3024 | 0.9438 |
| 0.0977 | 4.75 | 20000 | 0.2674 | 0.9495 |
| 0.0897 | 4.87 | 20500 | 0.3993 | 0.9300 |
| 0.1013 | 4.99 | 21000 | 0.3227 | 0.9289 |
| 0.0671 | 5.11 | 21500 | 0.3374 | 0.9427 |
| 0.0671 | 5.23 | 22000 | 0.4108 | 0.9278 |
| 0.0652 | 5.34 | 22500 | 0.3550 | 0.9381 |
| 0.0664 | 5.46 | 23000 | 0.3398 | 0.9358 |
| 0.0742 | 5.58 | 23500 | 0.3286 | 0.9381 |
| 0.0758 | 5.7 | 24000 | 0.3276 | 0.9312 |
| 0.075 | 5.82 | 24500 | 0.3202 | 0.9369 |
| 0.0686 | 5.94 | 25000 | 0.3481 | 0.9415 |
| 0.0729 | 6.06 | 25500 | 0.3816 | 0.9335 |
| 0.0568 | 6.18 | 26000 | 0.3132 | 0.9381 |
| 0.0529 | 6.29 | 26500 | 0.3757 | 0.9300 |
| 0.0506 | 6.41 | 27000 | 0.3396 | 0.9381 |
| 0.0476 | 6.53 | 27500 | 0.3642 | 0.9404 |
| 0.0555 | 6.65 | 28000 | 0.3430 | 0.9404 |
| 0.0574 | 6.77 | 28500 | 0.3401 | 0.9392 |
| 0.0524 | 6.89 | 29000 | 0.3378 | 0.9346 |
| 0.0492 | 7.01 | 29500 | 0.3833 | 0.9381 |
| 0.039 | 7.13 | 30000 | 0.3347 | 0.9346 |
| 0.0411 | 7.24 | 30500 | 0.4404 | 0.9335 |
| 0.0412 | 7.36 | 31000 | 0.3618 | 0.9381 |
| 0.0477 | 7.48 | 31500 | 0.3806 | 0.9381 |
| 0.0435 | 7.6 | 32000 | 0.3912 | 0.9335 |
| 0.0443 | 7.72 | 32500 | 0.3900 | 0.9392 |
| 0.0421 | 7.84 | 33000 | 0.4152 | 0.9369 |
| 0.0495 | 7.96 | 33500 | 0.3832 | 0.9289 |
| 0.0293 | 8.08 | 34000 | 0.4427 | 0.9346 |
| 0.0253 | 8.19 | 34500 | 0.4425 | 0.9381 |
| 0.0407 | 8.31 | 35000 | 0.4102 | 0.9358 |
| 0.0311 | 8.43 | 35500 | 0.4447 | 0.9369 |
| 0.0291 | 8.55 | 36000 | 0.4612 | 0.9346 |
| 0.035 | 8.67 | 36500 | 0.4241 | 0.9346 |
| 0.0381 | 8.79 | 37000 | 0.4198 | 0.9312 |
| 0.0234 | 8.91 | 37500 | 0.4345 | 0.9369 |
| 0.0311 | 9.03 | 38000 | 0.4558 | 0.9312 |
| 0.028 | 9.14 | 38500 | 0.4245 | 0.9381 |
| 0.0213 | 9.26 | 39000 | 0.4462 | 0.9381 |
| 0.0276 | 9.38 | 39500 | 0.4210 | 0.9381 |
| 0.0183 | 9.5 | 40000 | 0.4310 | 0.9404 |
| 0.0184 | 9.62 | 40500 | 0.4437 | 0.9404 |
| 0.0296 | 9.74 | 41000 | 0.4311 | 0.9392 |
| 0.019 | 9.86 | 41500 | 0.4244 | 0.9415 |
| 0.0245 | 9.98 | 42000 | 0.4270 | 0.9415 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.7.1
- Datasets 1.18.3
- Tokenizers 0.11.6
|