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---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: wk3ex_bert_imdb_sentiment
results: []
datasets:
Kaggle imdb dataseg
# wk3ex_bert_imdb_sentiment
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:
- Loss: 0.2804
- Accuracy: 0.9201
- Precision: 0.9201
- Recall: 0.9201
- F1: 0.9201
## Model description
Exercise for University course. Finetuning for sentiment analysis with imdb Kaggle dataset
## Intended uses & limitations
Sentiment analysis
## Training and evaluation data
finetuning with imdb dataset
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2405 | 1.0 | 2500 | 0.2392 | 0.9093 | 0.9107 | 0.9093 | 0.9092 |
| 0.1183 | 2.0 | 5000 | 0.2804 | 0.9201 | 0.9201 | 0.9201 | 0.9201 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0 |