---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
- f1
model-index:
- name: scenario-TCR_data-AmazonScience_massive_all_1_1
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: massive
      type: massive
      config: all_1.1
      split: validation
      args: all_1.1
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8558780127889818
    - name: F1
      type: f1
      value: 0.8318635435156069
---

<!-- 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-TCR_data-AmazonScience_massive_all_1_1

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: 0.9483
- Accuracy: 0.8559
- F1: 0.8319

## 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: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.519         | 0.27  | 5000  | 0.6915          | 0.8379   | 0.7941 |
| 0.3806        | 0.53  | 10000 | 0.6969          | 0.8468   | 0.8063 |
| 0.3259        | 0.8   | 15000 | 0.6916          | 0.8515   | 0.8159 |
| 0.2379        | 1.07  | 20000 | 0.7826          | 0.8505   | 0.8191 |
| 0.236         | 1.34  | 25000 | 0.7514          | 0.8508   | 0.8189 |
| 0.2298        | 1.6   | 30000 | 0.7719          | 0.8526   | 0.8267 |
| 0.2169        | 1.87  | 35000 | 0.8162          | 0.8505   | 0.8265 |
| 0.164         | 2.14  | 40000 | 0.8316          | 0.8549   | 0.8272 |
| 0.1684        | 2.41  | 45000 | 0.8123          | 0.8513   | 0.8204 |
| 0.158         | 2.67  | 50000 | 0.8252          | 0.8556   | 0.8309 |
| 0.1761        | 2.94  | 55000 | 0.8092          | 0.8545   | 0.8287 |
| 0.1378        | 3.21  | 60000 | 0.8574          | 0.8607   | 0.8357 |
| 0.1399        | 3.47  | 65000 | 0.8976          | 0.8572   | 0.8359 |
| 0.1431        | 3.74  | 70000 | 0.8908          | 0.8536   | 0.8350 |
| 0.1249        | 4.01  | 75000 | 0.9613          | 0.8533   | 0.8292 |
| 0.1129        | 4.28  | 80000 | 0.9511          | 0.8543   | 0.8306 |
| 0.1143        | 4.54  | 85000 | 0.9001          | 0.8563   | 0.8331 |
| 0.122         | 4.81  | 90000 | 0.9483          | 0.8559   | 0.8319 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3