---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
- f1
model-index:
- name: scenario-MDBT-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: 0.8577887926141738
    - name: F1
      type: f1
      value: 0.8335554213502777
---

<!-- 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-MDBT-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: 0.9178
- Accuracy: 0.8578
- F1: 0.8336

## 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: 64
- seed: 66
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.5269        | 0.27  | 5000  | 0.6875          | 0.8358   | 0.7817 |
| 0.3683        | 0.53  | 10000 | 0.6940          | 0.8489   | 0.8131 |
| 0.3073        | 0.8   | 15000 | 0.6710          | 0.8545   | 0.8198 |
| 0.2189        | 1.07  | 20000 | 0.7507          | 0.8539   | 0.8299 |
| 0.2276        | 1.34  | 25000 | 0.7456          | 0.8582   | 0.8347 |
| 0.1939        | 1.6   | 30000 | 0.8157          | 0.8562   | 0.8342 |
| 0.1852        | 1.87  | 35000 | 0.7920          | 0.8548   | 0.8269 |
| 0.1302        | 2.14  | 40000 | 0.8574          | 0.8559   | 0.8329 |
| 0.1273        | 2.41  | 45000 | 0.8945          | 0.8594   | 0.8330 |
| 0.1163        | 2.67  | 50000 | 0.9178          | 0.8578   | 0.8336 |


### Framework versions

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