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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_155
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: 0.8146070604260471
name: Accuracy
- type: f1
value: 0.7894820718803818
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_155
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: 1.9705
- Accuracy: 0.8146
- F1: 0.7895
## 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: 55
- 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 |
|:-------------:|:------:|:------:|:---------------:|:--------:|:------:|
| 1.2731 | 0.2672 | 5000 | 1.2901 | 0.6543 | 0.5621 |
| 0.9748 | 0.5344 | 10000 | 0.9900 | 0.7385 | 0.6906 |
| 0.8375 | 0.8017 | 15000 | 0.8798 | 0.7648 | 0.7104 |
| 0.5776 | 1.0689 | 20000 | 0.8718 | 0.7814 | 0.7300 |
| 0.5844 | 1.3361 | 25000 | 0.8115 | 0.7917 | 0.7475 |
| 0.5128 | 1.6033 | 30000 | 0.8029 | 0.7986 | 0.7618 |
| 0.5164 | 1.8706 | 35000 | 0.7975 | 0.7978 | 0.7526 |
| 0.3296 | 2.1378 | 40000 | 0.8562 | 0.8030 | 0.7669 |
| 0.356 | 2.4050 | 45000 | 0.8397 | 0.8052 | 0.7660 |
| 0.3481 | 2.6722 | 50000 | 0.8293 | 0.8111 | 0.7798 |
| 0.3435 | 2.9394 | 55000 | 0.8290 | 0.8091 | 0.7780 |
| 0.2186 | 3.2067 | 60000 | 0.9522 | 0.8106 | 0.7777 |
| 0.2362 | 3.4739 | 65000 | 0.9482 | 0.8115 | 0.7782 |
| 0.2341 | 3.7411 | 70000 | 0.9290 | 0.8097 | 0.7801 |
| 0.2062 | 4.0083 | 75000 | 0.9605 | 0.8145 | 0.7868 |
| 0.1568 | 4.2756 | 80000 | 1.0468 | 0.8117 | 0.7825 |
| 0.1572 | 4.5428 | 85000 | 1.1166 | 0.8109 | 0.7838 |
| 0.1591 | 4.8100 | 90000 | 1.0949 | 0.8111 | 0.7859 |
| 0.0872 | 5.0772 | 95000 | 1.2311 | 0.8129 | 0.7868 |
| 0.0978 | 5.3444 | 100000 | 1.3205 | 0.8064 | 0.7780 |
| 0.104 | 5.6117 | 105000 | 1.2794 | 0.8124 | 0.7842 |
| 0.1035 | 5.8789 | 110000 | 1.2706 | 0.8140 | 0.7871 |
| 0.0615 | 6.1461 | 115000 | 1.4577 | 0.8114 | 0.7851 |
| 0.0692 | 6.4133 | 120000 | 1.4930 | 0.8097 | 0.7866 |
| 0.0662 | 6.6806 | 125000 | 1.5160 | 0.8125 | 0.7887 |
| 0.0685 | 6.9478 | 130000 | 1.5319 | 0.8124 | 0.7873 |
| 0.0481 | 7.2150 | 135000 | 1.6618 | 0.8107 | 0.7871 |
| 0.0448 | 7.4822 | 140000 | 1.7140 | 0.8119 | 0.7864 |
| 0.0405 | 7.7495 | 145000 | 1.7438 | 0.8141 | 0.7894 |
| 0.0303 | 8.0167 | 150000 | 1.8255 | 0.8116 | 0.7850 |
| 0.025 | 8.2839 | 155000 | 1.8547 | 0.8135 | 0.7898 |
| 0.0302 | 8.5511 | 160000 | 1.8674 | 0.8150 | 0.7891 |
| 0.0293 | 8.8183 | 165000 | 1.8820 | 0.8131 | 0.7890 |
| 0.0177 | 9.0856 | 170000 | 1.9414 | 0.8140 | 0.7906 |
| 0.0164 | 9.3528 | 175000 | 1.9824 | 0.8130 | 0.7898 |
| 0.019 | 9.6200 | 180000 | 1.9458 | 0.8139 | 0.7889 |
| 0.0203 | 9.8872 | 185000 | 1.9705 | 0.8146 | 0.7895 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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