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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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metrics: |
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- f1 |
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model-index: |
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- name: Finetuned-Roberta-Base-Sentiment-identifier |
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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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# Finetuned-Roberta-Base-Sentiment-identifier |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7332 |
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- F1: 0.6622 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.8545 | 0.5 | 500 | 0.8251 | 0.6428 | |
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| 0.7952 | 1.0 | 1000 | 0.7831 | 0.6445 | |
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| 0.7962 | 1.5 | 1500 | 0.7935 | 0.6495 | |
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| 0.7669 | 2.01 | 2000 | 0.7544 | 0.6520 | |
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| 0.7468 | 2.51 | 2500 | 0.7614 | 0.6724 | |
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| 0.76 | 3.01 | 3000 | 0.7332 | 0.6622 | |
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| 0.7352 | 3.51 | 3500 | 0.8651 | 0.6036 | |
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| 0.7454 | 4.01 | 4000 | 0.7420 | 0.6584 | |
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| 0.7302 | 4.51 | 4500 | 0.7652 | 0.6573 | |
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| 0.7099 | 5.02 | 5000 | 0.7372 | 0.6697 | |
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| 0.73 | 5.52 | 5500 | 0.7806 | 0.6654 | |
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| 0.7265 | 6.02 | 6000 | 0.7476 | 0.6656 | |
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| 0.7092 | 6.52 | 6500 | 0.7632 | 0.6535 | |
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| 0.7322 | 7.02 | 7000 | 0.8017 | 0.6126 | |
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| 0.7168 | 7.52 | 7500 | 0.8046 | 0.6711 | |
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| 0.7279 | 8.02 | 8000 | 0.7734 | 0.6652 | |
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| 0.6884 | 8.53 | 8500 | 0.7806 | 0.6662 | |
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| 0.6942 | 9.03 | 9000 | 0.7790 | 0.6670 | |
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| 0.6865 | 9.53 | 9500 | 0.7835 | 0.6650 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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