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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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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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base_model: bert-base-uncased |
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model-index: |
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- name: finetuning-sentiment-analysis-en |
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results: [] |
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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-analysis-en |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0792 |
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- Accuracy: 0.9803 |
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- F1: 0.9856 |
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- Precision: 0.9875 |
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- Recall: 0.9837 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.426 | 1.0 | 1408 | 0.2718 | 0.8910 | 0.9201 | 0.9251 | 0.9151 | |
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| 0.3247 | 2.0 | 2816 | 0.1552 | 0.9540 | 0.9665 | 0.9656 | 0.9674 | |
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| 0.1582 | 3.0 | 4224 | 0.0792 | 0.9803 | 0.9856 | 0.9875 | 0.9837 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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