---
language:
- en
license: mit
tags:
- generated_from_trainer
- nlu
- intent-classification
datasets:
- AmazonScience/massive
metrics:
- accuracy
- f1
pipeline_tag: text-classification
base_model: microsoft/mdeberta-v3-base
model-index:
- name: mdeberta-v3-base_amazon-massive_intent
  results:
  - task:
      type: intent-classification
      name: intent-classification
    dataset:
      name: MASSIVE
      type: AmazonScience/massive
      split: test
    metrics:
    - type: f1
      value: 0.8136
      name: F1
---

<!-- 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. -->

# mdeberta-v3-base_amazon-massive_intent

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the [MASSIVE1.1](https://huggingface.co/datasets/AmazonScience/massive) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1661
- Accuracy: 0.8136
- F1: 0.8136

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 3.6412        | 1.0   | 720   | 2.7536          | 0.3123   | 0.3123 |
| 2.8575        | 2.0   | 1440  | 1.8556          | 0.5303   | 0.5303 |
| 1.7284        | 3.0   | 2160  | 1.3758          | 0.6699   | 0.6699 |
| 1.3794        | 4.0   | 2880  | 1.1221          | 0.7236   | 0.7236 |
| 0.942         | 5.0   | 3600  | 0.9936          | 0.7609   | 0.7609 |
| 0.7672        | 6.0   | 4320  | 0.9411          | 0.7727   | 0.7727 |
| 0.602         | 7.0   | 5040  | 0.9196          | 0.7841   | 0.7841 |
| 0.4776        | 8.0   | 5760  | 0.9328          | 0.7895   | 0.7895 |
| 0.4347        | 9.0   | 6480  | 0.9602          | 0.7860   | 0.7860 |
| 0.2941        | 10.0  | 7200  | 0.9543          | 0.7949   | 0.7949 |
| 0.2783        | 11.0  | 7920  | 0.9979          | 0.8013   | 0.8013 |
| 0.2038        | 12.0  | 8640  | 0.9702          | 0.8062   | 0.8062 |
| 0.1827        | 13.0  | 9360  | 1.0121          | 0.8106   | 0.8106 |
| 0.1352        | 14.0  | 10080 | 1.0339          | 0.8136   | 0.8136 |
| 0.1115        | 15.0  | 10800 | 1.1091          | 0.8057   | 0.8057 |
| 0.0996        | 16.0  | 11520 | 1.1134          | 0.8151   | 0.8151 |
| 0.0837        | 17.0  | 12240 | 1.1288          | 0.8160   | 0.8160 |
| 0.0711        | 18.0  | 12960 | 1.1499          | 0.8155   | 0.8155 |
| 0.0594        | 19.0  | 13680 | 1.1622          | 0.8126   | 0.8126 |
| 0.0569        | 20.0  | 14400 | 1.1661          | 0.8136   | 0.8136 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2