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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- imdb
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metrics:
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- accuracy
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- f1
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model-index:
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- name: finetuned-base_mini
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: imdb
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type: imdb
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config: plain_text
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split: train
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args: plain_text
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9076
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- name: F1
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type: f1
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value: 0.9515621723631789
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# finetuned-base_mini
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This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the imdb dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3938
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- Accuracy: 0.9076
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- F1: 0.9516
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 3e-05
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- train_batch_size: 128
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- eval_batch_size: 128
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: constant
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- num_epochs: 200
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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| 0.354 | 2.55 | 500 | 0.2300 | 0.9116 | 0.9538 |
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| 0.2086 | 5.1 | 1000 | 0.3182 | 0.8815 | 0.9370 |
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| 0.1401 | 7.65 | 1500 | 0.2160 | 0.9241 | 0.9605 |
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| 0.0902 | 10.2 | 2000 | 0.4684 | 0.8722 | 0.9317 |
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| 0.0654 | 12.76 | 2500 | 0.4885 | 0.8747 | 0.9332 |
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| 0.043 | 15.31 | 3000 | 0.3938 | 0.9076 | 0.9516 |
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### Framework versions
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- Transformers 4.25.0
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- Pytorch 1.12.1+cu113
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- Datasets 2.7.1
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- Tokenizers 0.13.2
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