---
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: 0.8643440917174317
    - name: F1
      type: f1
      value: 0.8368032657773605
---

<!-- 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: 1.0026
- Accuracy: 0.8643
- F1: 0.8368

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.5131        | 0.27  | 5000  | 0.6674          | 0.8368   | 0.7780 |
| 0.3715        | 0.53  | 10000 | 0.6554          | 0.8527   | 0.8145 |
| 0.3066        | 0.8   | 15000 | 0.6924          | 0.8471   | 0.8103 |
| 0.2194        | 1.07  | 20000 | 0.7348          | 0.8548   | 0.8238 |
| 0.2112        | 1.34  | 25000 | 0.7297          | 0.8581   | 0.8288 |
| 0.1907        | 1.6   | 30000 | 0.7308          | 0.8558   | 0.8288 |
| 0.1816        | 1.87  | 35000 | 0.7785          | 0.8565   | 0.8281 |
| 0.1297        | 2.14  | 40000 | 0.8493          | 0.8567   | 0.8278 |
| 0.127         | 2.41  | 45000 | 0.8757          | 0.8576   | 0.8310 |
| 0.1148        | 2.67  | 50000 | 0.8581          | 0.8577   | 0.8300 |
| 0.1287        | 2.94  | 55000 | 0.8479          | 0.8597   | 0.8341 |
| 0.0875        | 3.21  | 60000 | 0.8763          | 0.8656   | 0.8392 |
| 0.0832        | 3.47  | 65000 | 0.9379          | 0.8620   | 0.8341 |
| 0.0837        | 3.74  | 70000 | 0.9044          | 0.8625   | 0.8339 |
| 0.0617        | 4.01  | 75000 | 0.9840          | 0.8618   | 0.8352 |
| 0.0524        | 4.28  | 80000 | 0.9955          | 0.8639   | 0.8385 |
| 0.0496        | 4.54  | 85000 | 1.0026          | 0.8643   | 0.8368 |


### Framework versions

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