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--- |
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license: mit |
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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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model-index: |
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- name: bert_financial_phrasebank |
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results: [] |
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datasets: |
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- financial_phrasebank |
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library_name: transformers |
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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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# tps_sentimental_analysis |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the financial_phrasebank dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2586 |
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- Accuracy: 0.9604 |
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## Model description |
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A fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) |
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## Intended uses & limitations |
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Sentimental Analysis |
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| Lines | Emotions | |
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|:-----:|:--------:| |
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|Hi, Harper. I’m really happy you came.| Positive | |
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|Happy Father’s Day.| Positive | |
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|It was Christmas.| Neutral | |
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|HARPER sits at a table alone in a room.| Neutral | |
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|I am mad at you badly.| Negative | |
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## Training and evaluation data |
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financial_phrasebank |
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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: 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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 114 | 0.5293 | 0.8230 | |
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| No log | 2.0 | 228 | 0.0804 | 0.9779 | |
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| No log | 3.0 | 342 | 0.0367 | 0.9867 | |
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| No log | 4.0 | 456 | 0.1544 | 0.9646 | |
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| 0.3241 | 5.0 | 570 | 0.0497 | 0.9912 | |
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| 0.3241 | 6.0 | 684 | 0.0520 | 0.9912 | |
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| 0.3241 | 7.0 | 798 | 0.0318 | 0.9912 | |
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| 0.3241 | 8.0 | 912 | 0.0628 | 0.9912 | |
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| 0.0218 | 9.0 | 1026 | 0.0777 | 0.9867 | |
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| 0.0218 | 10.0 | 1140 | 0.0866 | 0.9867 | |
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### Framework versions |
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- Transformers 4.30.1 |
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- Pytorch 2.1.0+cu118 |
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- Tokenizers 0.13.3 |
