alarm_query alarm_remove alarm_set audio_volume_down audio_volume_mute audio_volume_other audio_volume_up calendar_query calendar_remove calendar_set cooking_query cooking_recipe datetime_convert datetime_query email_addcontact email_query email_querycontact email_sendemail general_greet general_joke general_quirky iot_cleaning iot_coffee iot_hue_lightchange iot_hue_lightdim iot_hue_lightoff iot_hue_lighton iot_hue_lightup iot_wemo_off iot_wemo_on lists_createoradd lists_query lists_remove music_dislikeness music_likeness music_query music_settings news_query play_audiobook play_game play_music play_podcasts play_radio qa_currency qa_definition qa_factoid qa_maths qa_stock recommendation_events recommendation_locations recommendation_movies social_post social_query takeaway_order takeaway_query transport_query transport_taxi transport_ticket transport_traffic weather_query
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-massive-intent-detection-english
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: massive
      type: massive
      args: en-US
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.886684599865501
---

<!-- 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. -->

# distilbert-base-uncased-finetuned-massive-intent-detection-english

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/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
