transformers pytorch safetensors roformer text-classification generated_from_trainer dataset:imdb base_model:versae/gzip-bert base_model:finetune:versae/gzip-bert license:openrail model-index autotrain_compatible endpoints_compatible region:us
---
base_model: versae/gzip-bert
datasets:
- imdb
license: openrail
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: gzipbert_imdb_rpe_250k_v4
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: imdb
      type: imdb
      config: plain_text
      split: test
      args: plain_text
    metrics:
    - type: accuracy
      value: 0.49512
      name: Accuracy
---

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# gzipbert_imdb_rpe_250k_v4

This model is a fine-tuned version of [versae/gzip-bert](https://huggingface.co/versae/gzip-bert) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4034
- Accuracy: 0.4951

## 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: 1e-06
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0425        | 1.0   | 1563 | 2.5908          | 0.5231   |
| 0.0357        | 2.0   | 3126 | 2.8405          | 0.5343   |
| 0.0291        | 3.0   | 4689 | 2.8759          | 0.5562   |
| 0.0313        | 4.0   | 6252 | 3.1563          | 0.5257   |
| 0.0288        | 5.0   | 7815 | 3.4034          | 0.4951   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
