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JeffreyJIANG
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bert-imdb

Text Classification
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bert-imdb
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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-imdb
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: imdb
      type: imdb
      config: plain_text
      split: test
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.93956
    - name: F1
      type: f1
      value: 0.9395537111681099
    - name: Precision
      type: precision
      value: 0.939743003448315
    - name: Recall
      type: recall
      value: 0.93956
---

<!-- 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-imdb

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2266
- Accuracy: 0.9396
- F1: 0.9396
- Precision: 0.9397
- Recall: 0.9396

## 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: 9072
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2223        | 1.0   | 1563 | 0.1898          | 0.9328   | 0.9327 | 0.9331    | 0.9328 |
| 0.1161        | 2.0   | 3126 | 0.2266          | 0.9396   | 0.9396 | 0.9397    | 0.9396 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2