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---
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 |