---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
model-index:
- name: bert-base-uncased_finetuning
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: massive
      type: massive
      config: en-US
      split: validation
      args: en-US
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8648285137861466
---

<!-- 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_finetuning

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5749
- Accuracy: 0.8648

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1227        | 1.39  | 500  | 0.7218          | 0.8416   |
| 0.4828        | 2.78  | 1000 | 0.5573          | 0.8775   |
| 0.2355        | 4.17  | 1500 | 0.6613          | 0.8697   |
| 0.1334        | 5.56  | 2000 | 0.6672          | 0.8800   |
| 0.0848        | 6.94  | 2500 | 0.7778          | 0.8800   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.0