---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
- f1
model-index:
- name: scenario-MDBT-TCR_data-cl-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: 0.8000036435845584
    - name: F1
      type: f1
      value: 0.758305292124411
---

<!-- 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-cl-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: 1.3801
- Accuracy: 0.8000
- F1: 0.7583

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.489         | 0.56  | 5000  | 0.9200          | 0.7871   | 0.7332 |
| 0.2753        | 1.11  | 10000 | 1.0539          | 0.7885   | 0.7364 |
| 0.2348        | 1.67  | 15000 | 1.0362          | 0.7891   | 0.7431 |
| 0.1648        | 2.22  | 20000 | 1.1925          | 0.7867   | 0.7535 |
| 0.1481        | 2.78  | 25000 | 1.1608          | 0.7920   | 0.7513 |
| 0.1074        | 3.33  | 30000 | 1.3151          | 0.7966   | 0.7513 |
| 0.0942        | 3.89  | 35000 | 1.3274          | 0.7952   | 0.7523 |
| 0.0684        | 4.45  | 40000 | 1.3801          | 0.8000   | 0.7583 |


### Framework versions

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