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---
license: openrail
base_model: versae/gzip-bert
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: gzipbert_imdb_rpe_250k
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: 0.50952
---
<!-- 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. -->
# gzipbert_imdb_rpe_250k
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: 6.0866
- Accuracy: 0.5095
## 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-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.003 | 1.0 | 1563 | 5.1727 | 0.5548 |
| 0.0061 | 2.0 | 3126 | 5.7975 | 0.5176 |
| 0.0056 | 3.0 | 4689 | 5.6762 | 0.5107 |
| 0.0019 | 4.0 | 6252 | 6.0355 | 0.5082 |
| 0.0043 | 5.0 | 7815 | 6.0866 | 0.5095 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
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