metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
base_model: distilbert-base-uncased
model-index:
- name: distilbert-base-uncased-finetuned-massive-intent-detection-english
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: massive
type: massive
args: en-US
metrics:
- type: accuracy
value: 0.886684599865501
name: Accuracy
distilbert-base-uncased-finetuned-massive-intent-detection-english
This model is a fine-tuned version of distilbert-base-uncased on the massive dataset. It achieves the following results on the evaluation set:
- Loss: 0.4873
- Accuracy: 0.8867
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.5849 | 1.0 | 360 | 1.3826 | 0.7359 |
| 1.0662 | 2.0 | 720 | 0.7454 | 0.8357 |
| 0.5947 | 3.0 | 1080 | 0.5668 | 0.8642 |
| 0.3824 | 4.0 | 1440 | 0.5007 | 0.8770 |
| 0.2649 | 5.0 | 1800 | 0.4829 | 0.8824 |
| 0.1877 | 6.0 | 2160 | 0.4843 | 0.8824 |
| 0.1377 | 7.0 | 2520 | 0.4858 | 0.8834 |
| 0.1067 | 8.0 | 2880 | 0.4924 | 0.8864 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1