---
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: 0.7999125539705962
    - name: F1
      type: f1
      value: 0.7608456488954072
---

<!-- 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: 1.3792
- Accuracy: 0.7999
- F1: 0.7608

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.4658        | 0.56  | 5000  | 0.9703          | 0.7825   | 0.7290 |
| 0.2748        | 1.11  | 10000 | 0.9829          | 0.7934   | 0.7386 |
| 0.237         | 1.67  | 15000 | 1.0459          | 0.7881   | 0.7348 |
| 0.1545        | 2.22  | 20000 | 1.1641          | 0.7920   | 0.7544 |
| 0.1482        | 2.78  | 25000 | 1.1840          | 0.7951   | 0.7528 |
| 0.1076        | 3.33  | 30000 | 1.2621          | 0.7933   | 0.7504 |
| 0.0974        | 3.89  | 35000 | 1.3127          | 0.7972   | 0.7566 |
| 0.0654        | 4.45  | 40000 | 1.3792          | 0.7999   | 0.7608 |


### Framework versions

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