End of training
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README.md
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---
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license: mit
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base_model: roberta-base
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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: finetuning-sentiment-model-roberta-base-25000-samples
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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: 0.9476
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- name: F1
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type: f1
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value: 0.9488481062085123
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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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# finetuning-sentiment-model-roberta-base-25000-samples
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imdb dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3321
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- Accuracy: 0.9476
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- F1: 0.9488
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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: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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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: linear
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- num_epochs: 5
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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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| 0.2475 | 1.0 | 1407 | 0.2287 | 0.936 | 0.9383 |
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| 0.1528 | 2.0 | 2814 | 0.2354 | 0.9328 | 0.9319 |
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| 0.0888 | 3.0 | 4221 | 0.2754 | 0.9432 | 0.9452 |
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| 0.0476 | 4.0 | 5628 | 0.2962 | 0.9464 | 0.9475 |
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| 0.0275 | 5.0 | 7035 | 0.3321 | 0.9476 | 0.9488 |
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### Framework versions
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- Transformers 4.34.1
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- Pytorch 2.1.0+cu118
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- Datasets 2.14.6
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- Tokenizers 0.14.1
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