transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:massive license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- massive
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: BERT-tiny-Massive-intent
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: massive
      type: massive
      config: en-US
      split: train
      args: en-US
    metrics:
    - type: accuracy
      value: 0.8475159862272503
      name: Accuracy
---

<!-- 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. -->

# BERT-tiny-Massive-intent

This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6740
- Accuracy: 0.8475

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 3.6104        | 1.0   | 720   | 3.0911          | 0.3601   |
| 2.8025        | 2.0   | 1440  | 2.3800          | 0.5165   |
| 2.2292        | 3.0   | 2160  | 1.9134          | 0.5991   |
| 1.818         | 4.0   | 2880  | 1.5810          | 0.6744   |
| 1.5171        | 5.0   | 3600  | 1.3522          | 0.7108   |
| 1.2876        | 6.0   | 4320  | 1.1686          | 0.7442   |
| 1.1049        | 7.0   | 5040  | 1.0355          | 0.7683   |
| 0.9623        | 8.0   | 5760  | 0.9466          | 0.7885   |
| 0.8424        | 9.0   | 6480  | 0.8718          | 0.7875   |
| 0.7473        | 10.0  | 7200  | 0.8107          | 0.8028   |
| 0.6735        | 11.0  | 7920  | 0.7710          | 0.8180   |
| 0.6085        | 12.0  | 8640  | 0.7404          | 0.8210   |
| 0.5536        | 13.0  | 9360  | 0.7180          | 0.8229   |
| 0.5026        | 14.0  | 10080 | 0.6980          | 0.8318   |
| 0.4652        | 15.0  | 10800 | 0.6970          | 0.8337   |
| 0.4234        | 16.0  | 11520 | 0.6822          | 0.8372   |
| 0.3987        | 17.0  | 12240 | 0.6691          | 0.8436   |
| 0.3707        | 18.0  | 12960 | 0.6679          | 0.8455   |
| 0.3433        | 19.0  | 13680 | 0.6740          | 0.8475   |
| 0.3206        | 20.0  | 14400 | 0.6760          | 0.8451   |
| 0.308         | 21.0  | 15120 | 0.6704          | 0.8436   |
| 0.2813        | 22.0  | 15840 | 0.6701          | 0.8416   |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
