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---
base_model: microsoft/mdeberta-v3-base
datasets:
- massive
library_name: transformers
license: mit
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: scenario-NON-KD-SCR-D2_data-AmazonScience_massive_all_1_144
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: massive
type: massive
config: all_1.1
split: validation
args: all_1.1
metrics:
- type: accuracy
value: 0.8158178516024065
name: Accuracy
- type: f1
value: 0.790068262479823
name: F1
---
<!-- 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. -->
# scenario-NON-KD-SCR-D2_data-AmazonScience_massive_all_1_144
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9525
- Accuracy: 0.8158
- F1: 0.7901
## 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: 44
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:------:|:---------------:|:--------:|:------:|
| 1.2857 | 0.2672 | 5000 | 1.2982 | 0.6464 | 0.5527 |
| 0.9859 | 0.5344 | 10000 | 1.0042 | 0.7331 | 0.6625 |
| 0.8126 | 0.8017 | 15000 | 0.9054 | 0.7613 | 0.6991 |
| 0.5568 | 1.0689 | 20000 | 0.8391 | 0.7828 | 0.7319 |
| 0.5531 | 1.3361 | 25000 | 0.8316 | 0.7886 | 0.7394 |
| 0.5497 | 1.6033 | 30000 | 0.7894 | 0.8011 | 0.7618 |
| 0.5099 | 1.8706 | 35000 | 0.7805 | 0.8050 | 0.7616 |
| 0.3327 | 2.1378 | 40000 | 0.8676 | 0.8040 | 0.7633 |
| 0.3482 | 2.4050 | 45000 | 0.8556 | 0.8060 | 0.7700 |
| 0.3506 | 2.6722 | 50000 | 0.8309 | 0.8087 | 0.7816 |
| 0.3508 | 2.9394 | 55000 | 0.8149 | 0.8105 | 0.7683 |
| 0.221 | 3.2067 | 60000 | 0.9645 | 0.8070 | 0.7760 |
| 0.222 | 3.4739 | 65000 | 0.9305 | 0.8113 | 0.7836 |
| 0.2414 | 3.7411 | 70000 | 0.9195 | 0.8122 | 0.7846 |
| 0.2032 | 4.0083 | 75000 | 0.9858 | 0.8141 | 0.7855 |
| 0.1457 | 4.2756 | 80000 | 1.0865 | 0.8130 | 0.7885 |
| 0.155 | 4.5428 | 85000 | 1.0413 | 0.8133 | 0.7830 |
| 0.1535 | 4.8100 | 90000 | 1.0934 | 0.8157 | 0.7887 |
| 0.0888 | 5.0772 | 95000 | 1.2135 | 0.8152 | 0.7896 |
| 0.0931 | 5.3444 | 100000 | 1.3402 | 0.8121 | 0.7857 |
| 0.1024 | 5.6117 | 105000 | 1.2838 | 0.8107 | 0.7848 |
| 0.1044 | 5.8789 | 110000 | 1.3039 | 0.8133 | 0.7885 |
| 0.0595 | 6.1461 | 115000 | 1.4268 | 0.8129 | 0.7877 |
| 0.0678 | 6.4133 | 120000 | 1.4729 | 0.8132 | 0.7866 |
| 0.0676 | 6.6806 | 125000 | 1.5201 | 0.8127 | 0.7859 |
| 0.0779 | 6.9478 | 130000 | 1.4956 | 0.8151 | 0.7905 |
| 0.0429 | 7.2150 | 135000 | 1.6860 | 0.8142 | 0.7897 |
| 0.0507 | 7.4822 | 140000 | 1.6751 | 0.8124 | 0.7842 |
| 0.0463 | 7.7495 | 145000 | 1.7002 | 0.8133 | 0.7866 |
| 0.034 | 8.0167 | 150000 | 1.7596 | 0.8135 | 0.7885 |
| 0.0254 | 8.2839 | 155000 | 1.8539 | 0.8133 | 0.7876 |
| 0.0294 | 8.5511 | 160000 | 1.8675 | 0.8146 | 0.7862 |
| 0.0296 | 8.8183 | 165000 | 1.8644 | 0.8142 | 0.7862 |
| 0.0174 | 9.0856 | 170000 | 1.9111 | 0.8151 | 0.7899 |
| 0.0159 | 9.3528 | 175000 | 1.9342 | 0.8156 | 0.7896 |
| 0.0171 | 9.6200 | 180000 | 1.9399 | 0.8161 | 0.7901 |
| 0.0209 | 9.8872 | 185000 | 1.9525 | 0.8158 | 0.7901 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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