---
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_166
  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: 0.8145597638957206
      name: Accuracy
    - type: f1
      value: 0.7901247763851288
      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_166

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: 1.9580
- Accuracy: 0.8146
- 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: 66
- 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     |
|:-------------:|:------:|:------:|:---------------:|:--------:|:------:|
| 1.2987        | 0.2672 | 5000   | 1.2851          | 0.6566   | 0.5593 |
| 0.9658        | 0.5344 | 10000  | 0.9963          | 0.7328   | 0.6604 |
| 0.808         | 0.8017 | 15000  | 0.8786          | 0.7661   | 0.7077 |
| 0.5621        | 1.0689 | 20000  | 0.8689          | 0.7807   | 0.7298 |
| 0.5504        | 1.3361 | 25000  | 0.8296          | 0.7897   | 0.7417 |
| 0.5334        | 1.6033 | 30000  | 0.8176          | 0.7940   | 0.7494 |
| 0.5188        | 1.8706 | 35000  | 0.7691          | 0.8056   | 0.7619 |
| 0.3426        | 2.1378 | 40000  | 0.8369          | 0.8057   | 0.7691 |
| 0.3362        | 2.4050 | 45000  | 0.8393          | 0.8053   | 0.7663 |
| 0.355         | 2.6722 | 50000  | 0.8216          | 0.8075   | 0.7742 |
| 0.3452        | 2.9394 | 55000  | 0.8262          | 0.8108   | 0.7767 |
| 0.2176        | 3.2067 | 60000  | 0.9217          | 0.8061   | 0.7754 |
| 0.2271        | 3.4739 | 65000  | 0.9242          | 0.8093   | 0.7800 |
| 0.2366        | 3.7411 | 70000  | 0.9183          | 0.8127   | 0.7835 |
| 0.2095        | 4.0083 | 75000  | 0.9789          | 0.8126   | 0.7802 |
| 0.146         | 4.2756 | 80000  | 1.0693          | 0.8097   | 0.7796 |
| 0.1526        | 4.5428 | 85000  | 1.0715          | 0.8119   | 0.7845 |
| 0.1606        | 4.8100 | 90000  | 1.0722          | 0.8150   | 0.7869 |
| 0.0916        | 5.0772 | 95000  | 1.2010          | 0.8157   | 0.7908 |
| 0.0957        | 5.3444 | 100000 | 1.2828          | 0.8112   | 0.7826 |
| 0.1065        | 5.6117 | 105000 | 1.2375          | 0.8146   | 0.7856 |
| 0.0993        | 5.8789 | 110000 | 1.2607          | 0.8141   | 0.7877 |
| 0.0583        | 6.1461 | 115000 | 1.4788          | 0.8122   | 0.7847 |
| 0.0688        | 6.4133 | 120000 | 1.4891          | 0.8117   | 0.7881 |
| 0.0673        | 6.6806 | 125000 | 1.5137          | 0.8129   | 0.7840 |
| 0.0781        | 6.9478 | 130000 | 1.5225          | 0.8110   | 0.7867 |
| 0.0453        | 7.2150 | 135000 | 1.6409          | 0.8132   | 0.7860 |
| 0.0487        | 7.4822 | 140000 | 1.6796          | 0.8125   | 0.7872 |
| 0.041         | 7.7495 | 145000 | 1.7356          | 0.8121   | 0.7864 |
| 0.035         | 8.0167 | 150000 | 1.7378          | 0.8139   | 0.7905 |
| 0.0293        | 8.2839 | 155000 | 1.8422          | 0.8118   | 0.7885 |
| 0.0289        | 8.5511 | 160000 | 1.8606          | 0.8126   | 0.7885 |
| 0.0271        | 8.8183 | 165000 | 1.8730          | 0.8140   | 0.7899 |
| 0.0186        | 9.0856 | 170000 | 1.9062          | 0.8139   | 0.7898 |
| 0.0197        | 9.3528 | 175000 | 1.9150          | 0.8142   | 0.7895 |
| 0.0274        | 9.6200 | 180000 | 1.9638          | 0.8133   | 0.7891 |
| 0.018         | 9.8872 | 185000 | 1.9580          | 0.8146   | 0.7901 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1