---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- nlu
- text-classification
datasets:
- AmazonScience/massive
metrics:
- accuracy
- f1
base_model: bert-base-uncased
model-index:
- name: bert-base-uncased-amazon-massive-intent
  results:
  - task:
      type: intent-classification
      name: intent-classification
    dataset:
      name: MASSIVE
      type: AmazonScience/massive
      split: test
    metrics:
    - type: f1
      value: 0.8903
      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. -->

# bert-base-uncased-amazon-massive-intent

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on 
[Amazon Massive](https://huggingface.co/datasets/AmazonScience/massive) dataset (only en-US subset).
It achieves the following results on the evaluation set:
- Loss: 0.4897
- Accuracy: 0.8903
- F1: 0.8903

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.5862        | 1.0   | 720  | 1.0160          | 0.8096   | 0.8096 |
| 1.0591        | 2.0   | 1440 | 0.6003          | 0.8716   | 0.8716 |
| 0.4151        | 3.0   | 2160 | 0.5113          | 0.8859   | 0.8859 |
| 0.3028        | 4.0   | 2880 | 0.5030          | 0.8883   | 0.8883 |
| 0.1852        | 5.0   | 3600 | 0.4897          | 0.8903   | 0.8903 |


### Framework versions

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