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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: mini-vanilla-target-imdb
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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: 0.87528
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- name: F1
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type: f1
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value: 0.9334925984386332
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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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# mini-vanilla-target-imdb
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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: 0.4773
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- Accuracy: 0.8753
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- F1: 0.9335
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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: 32
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- eval_batch_size: 32
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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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| 0.4272 | 0.64 | 500 | 0.2066 | 0.92 | 0.9583 |
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| 0.299 | 1.28 | 1000 | 0.2608 | 0.8906 | 0.9422 |
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| 0.2533 | 1.92 | 1500 | 0.1706 | 0.9337 | 0.9657 |
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| 0.2126 | 2.56 | 2000 | 0.3601 | 0.8576 | 0.9233 |
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| 0.1913 | 3.2 | 2500 | 0.3955 | 0.8594 | 0.9244 |
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| 0.1541 | 3.84 | 3000 | 0.1432 | 0.9484 | 0.9735 |
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| 0.1432 | 4.48 | 3500 | 0.2027 | 0.9346 | 0.9662 |
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| 0.1256 | 5.12 | 4000 | 0.3797 | 0.8898 | 0.9417 |
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| 0.1026 | 5.75 | 4500 | 0.4773 | 0.8753 | 0.9335 |
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### Framework versions
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- Transformers 4.25.1
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- Pytorch 1.12.1
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- Datasets 2.7.1
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- Tokenizers 0.13.2
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