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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- financial_phrasebank |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: finetuning-llms-project-2 |
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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: financial_phrasebank |
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type: financial_phrasebank |
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config: sentences_50agree |
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split: train |
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args: sentences_50agree |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.8330949180475766 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8493810178817056 |
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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-llms-project-2 |
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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: 0.5427 |
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- F1: 0.8331 |
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- Accuracy: 0.8494 |
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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: 32 |
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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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| |
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| 0.6137 | 0.94 | 100 | 0.5180 | 0.7614 | 0.8061 | |
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| 0.297 | 1.89 | 200 | 0.4018 | 0.8201 | 0.8425 | |
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| 0.1648 | 2.83 | 300 | 0.4641 | 0.8327 | 0.8521 | |
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| 0.0736 | 3.77 | 400 | 0.5427 | 0.8331 | 0.8494 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.1.0 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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