---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- general
model-index:
- name: liewchooichin/distilbert-base-uncased-tiny-imdb
  results: []
datasets:
- stanfordnlp/imdb
language:
- en
pipeline_tag: fill-mask
---

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# liewchooichin/distilbert-base-uncased-tiny-imdb

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.9373
- Validation Loss: 2.9930
- Epoch: 2

## Model description

This model is created from following the lesson in Hugging Face Learn. 
NLP -- Main NLP Tasks -- [Fine-tuning a masked language model](https://huggingface.co/learn/nlp-course/chapter7/3?fw=tf#the-dataset).

## Intended uses & limitations

This is only a small scale fine-tuning of the `standfordnlp/imbd` datasets. Only 1000 rows of the `unsupervised` dataset is used for training. 
The exercise is carried on Google Colab - T4 gpu.

## Training and evaluation data

1000 rows from the `standfordnlp/imbd` datasets.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -969, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.2484     | 3.2338          | 0     |
| 3.0821     | 2.8758          | 1     |
| 2.9373     | 2.9930          | 2     |


### Framework versions

- Transformers 4.40.2
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1