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--- |
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license: mit |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: microsoft/phi-2 |
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model-index: |
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- name: finetuned-phi2-financial-sentiment-analysis |
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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-phi2-financial-sentiment-analysis |
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This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the FinancialPhraseBank dataset. The FinancialPhraseBank dataset is a comprehensive collection that captures the sentiments of financial news headlines from the viewpoint of a retail investor. Comprising two key columns, namely "Sentiment" and "News Headline," the dataset effectively classifies sentiments as either negative, neutral, or positive. This structured dataset serves as a valuable resource for analyzing and understanding the complex dynamics of sentiment in the domain of financial news. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4052 |
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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: 0.0002 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 4 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.8067 | 1.0 | 112 | 1.5200 | |
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| 1.5055 | 2.0 | 225 | 1.4345 | |
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| 1.5221 | 3.0 | 337 | 1.4083 | |
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| 1.4956 | 3.98 | 448 | 1.4052 | |
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### Framework versions |
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- PEFT 0.7.1 |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
