transformers pytorch distilbert text-classification generated_from_trainer dataset:massive base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-uncased
datasets:
- massive
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: amazon_massive_intent-en-US-distilbert
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: massive
      type: massive
      config: en-US
      split: validation
      args: en-US
    metrics:
    - type: accuracy
      value: 0.8868666994589277
      name: Accuracy
---

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# amazon_massive_intent-en-US-distilbert

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: 0.7769
- Accuracy: 0.8869

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.1629        | 1.0   | 720   | 0.9624          | 0.7959   |
| 0.7179        | 2.0   | 1440  | 0.5693          | 0.8569   |
| 0.3695        | 3.0   | 2160  | 0.5104          | 0.8692   |
| 0.2227        | 4.0   | 2880  | 0.4813          | 0.8888   |
| 0.1302        | 5.0   | 3600  | 0.5207          | 0.8834   |
| 0.0822        | 6.0   | 4320  | 0.5709          | 0.8829   |
| 0.0527        | 7.0   | 5040  | 0.6016          | 0.8854   |
| 0.0345        | 8.0   | 5760  | 0.6373          | 0.8839   |
| 0.0244        | 9.0   | 6480  | 0.6616          | 0.8908   |
| 0.0191        | 10.0  | 7200  | 0.6958          | 0.8854   |
| 0.0131        | 11.0  | 7920  | 0.7212          | 0.8844   |
| 0.0095        | 12.0  | 8640  | 0.7348          | 0.8864   |
| 0.0067        | 13.0  | 9360  | 0.7502          | 0.8824   |
| 0.0055        | 14.0  | 10080 | 0.7469          | 0.8819   |
| 0.0038        | 15.0  | 10800 | 0.7772          | 0.8854   |
| 0.0043        | 16.0  | 11520 | 0.7761          | 0.8844   |
| 0.0025        | 17.0  | 12240 | 0.7625          | 0.8893   |
| 0.002         | 18.0  | 12960 | 0.7782          | 0.8854   |
| 0.0014        | 19.0  | 13680 | 0.7867          | 0.8859   |
| 0.0019        | 20.0  | 14400 | 0.7769          | 0.8869   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
