File size: 6,725 Bytes
7f594b6 c93ccee 7f594b6 c93ccee 7f594b6 c93ccee 7f594b6 c93ccee 7f594b6 c93ccee 7f594b6 c93ccee 7f594b6 c93ccee 7f594b6 |
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 |
---
license: mit
base_model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
tags:
- generated_from_trainer
datasets:
- sem_eval_2024_task_2
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sem_eval_2024_task_2
type: sem_eval_2024_task_2
config: sem_eval_2024_task_2_source
split: validation
args: sem_eval_2024_task_2_source
metrics:
- name: Accuracy
type: accuracy
value: 0.76
- name: Precision
type: precision
value: 0.7601040416166467
- name: Recall
type: recall
value: 0.76
- name: F1
type: f1
value: 0.75997599759976
---
<!-- 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. -->
# results2
This model is a fine-tuned version of [MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli](https://huggingface.co/MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli) on the sem_eval_2024_task_2 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1827
- Accuracy: 0.76
- Precision: 0.7601
- Recall: 0.76
- F1: 0.7600
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6925 | 1.0 | 107 | 0.6665 | 0.6 | 0.6457 | 0.6 | 0.5660 |
| 0.6729 | 2.0 | 214 | 0.6025 | 0.69 | 0.6964 | 0.69 | 0.6875 |
| 0.6857 | 3.0 | 321 | 0.6071 | 0.665 | 0.7531 | 0.665 | 0.6331 |
| 0.6667 | 4.0 | 428 | 0.5650 | 0.695 | 0.7157 | 0.6950 | 0.6875 |
| 0.6168 | 5.0 | 535 | 0.5036 | 0.75 | 0.7504 | 0.75 | 0.7499 |
| 0.5165 | 6.0 | 642 | 0.6248 | 0.67 | 0.6701 | 0.67 | 0.6700 |
| 0.4087 | 7.0 | 749 | 0.5246 | 0.735 | 0.7379 | 0.7350 | 0.7342 |
| 0.3083 | 8.0 | 856 | 0.6130 | 0.7 | 0.7 | 0.7 | 0.7 |
| 0.2909 | 9.0 | 963 | 0.7584 | 0.735 | 0.7723 | 0.7350 | 0.7256 |
| 0.319 | 10.0 | 1070 | 0.7350 | 0.72 | 0.7360 | 0.72 | 0.7152 |
| 0.1812 | 11.0 | 1177 | 0.9320 | 0.715 | 0.7176 | 0.7150 | 0.7141 |
| 0.2824 | 12.0 | 1284 | 0.9723 | 0.705 | 0.7336 | 0.7050 | 0.6957 |
| 0.2662 | 13.0 | 1391 | 0.8676 | 0.72 | 0.7222 | 0.72 | 0.7193 |
| 0.1641 | 14.0 | 1498 | 0.9450 | 0.71 | 0.7103 | 0.71 | 0.7099 |
| 0.2264 | 15.0 | 1605 | 1.1613 | 0.675 | 0.6764 | 0.675 | 0.6743 |
| 0.2077 | 16.0 | 1712 | 1.3497 | 0.715 | 0.7214 | 0.7150 | 0.7129 |
| 0.1767 | 17.0 | 1819 | 1.4154 | 0.705 | 0.7075 | 0.7050 | 0.7041 |
| 0.1751 | 18.0 | 1926 | 1.2369 | 0.735 | 0.7350 | 0.735 | 0.7350 |
| 0.1195 | 19.0 | 2033 | 1.1152 | 0.72 | 0.7334 | 0.72 | 0.7159 |
| 0.0507 | 20.0 | 2140 | 1.4853 | 0.715 | 0.7152 | 0.715 | 0.7149 |
| 0.0544 | 21.0 | 2247 | 1.7174 | 0.725 | 0.7302 | 0.7250 | 0.7234 |
| 0.0648 | 22.0 | 2354 | 1.7327 | 0.71 | 0.7121 | 0.71 | 0.7093 |
| 0.0039 | 23.0 | 2461 | 1.8211 | 0.725 | 0.7268 | 0.7250 | 0.7244 |
| 0.0153 | 24.0 | 2568 | 1.8315 | 0.715 | 0.7176 | 0.7150 | 0.7141 |
| 0.0017 | 25.0 | 2675 | 1.7446 | 0.72 | 0.7232 | 0.72 | 0.7190 |
| 0.0188 | 26.0 | 2782 | 1.6413 | 0.72 | 0.7274 | 0.72 | 0.7177 |
| 0.0168 | 27.0 | 2889 | 1.8013 | 0.73 | 0.7315 | 0.73 | 0.7296 |
| 0.0355 | 28.0 | 2996 | 2.0405 | 0.725 | 0.7354 | 0.725 | 0.7219 |
| 0.0168 | 29.0 | 3103 | 1.5087 | 0.735 | 0.7350 | 0.735 | 0.7350 |
| 0.0409 | 30.0 | 3210 | 1.5272 | 0.72 | 0.7244 | 0.72 | 0.7186 |
| 0.004 | 31.0 | 3317 | 1.9978 | 0.715 | 0.7214 | 0.7150 | 0.7129 |
| 0.0002 | 32.0 | 3424 | 1.9760 | 0.72 | 0.7244 | 0.72 | 0.7186 |
| 0.0111 | 33.0 | 3531 | 1.9985 | 0.74 | 0.7409 | 0.74 | 0.7398 |
| 0.052 | 34.0 | 3638 | 1.9607 | 0.73 | 0.7334 | 0.73 | 0.7290 |
| 0.0263 | 35.0 | 3745 | 1.7118 | 0.75 | 0.7525 | 0.75 | 0.7494 |
| 0.0101 | 36.0 | 3852 | 1.9553 | 0.755 | 0.7571 | 0.755 | 0.7545 |
| 0.0001 | 37.0 | 3959 | 2.0064 | 0.75 | 0.7537 | 0.75 | 0.7491 |
| 0.0186 | 38.0 | 4066 | 2.1726 | 0.74 | 0.7404 | 0.74 | 0.7399 |
| 0.0046 | 39.0 | 4173 | 2.1083 | 0.755 | 0.7550 | 0.755 | 0.7550 |
| 0.0042 | 40.0 | 4280 | 1.9944 | 0.76 | 0.7609 | 0.76 | 0.7598 |
| 0.0178 | 41.0 | 4387 | 2.0096 | 0.76 | 0.7604 | 0.76 | 0.7599 |
| 0.0089 | 42.0 | 4494 | 2.0431 | 0.765 | 0.7652 | 0.765 | 0.7649 |
| 0.0095 | 43.0 | 4601 | 2.0662 | 0.76 | 0.7604 | 0.76 | 0.7599 |
| 0.0162 | 44.0 | 4708 | 2.1703 | 0.745 | 0.7450 | 0.745 | 0.7450 |
| 0.0001 | 45.0 | 4815 | 2.1525 | 0.76 | 0.7601 | 0.76 | 0.7600 |
| 0.0001 | 46.0 | 4922 | 2.1581 | 0.76 | 0.7601 | 0.76 | 0.7600 |
| 0.0086 | 47.0 | 5029 | 2.1665 | 0.76 | 0.7601 | 0.76 | 0.7600 |
| 0.0088 | 48.0 | 5136 | 2.1747 | 0.76 | 0.7601 | 0.76 | 0.7600 |
| 0.0044 | 49.0 | 5243 | 2.1812 | 0.76 | 0.7601 | 0.76 | 0.7600 |
| 0.0043 | 50.0 | 5350 | 2.1827 | 0.76 | 0.7601 | 0.76 | 0.7600 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|