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---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/phi-2
model-index:
- name: finetuned-phi2-financial-sentiment-analysis
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-phi2-financial-sentiment-analysis
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.
It achieves the following results on the evaluation set:
- Loss: 1.4052
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8067 | 1.0 | 112 | 1.5200 |
| 1.5055 | 2.0 | 225 | 1.4345 |
| 1.5221 | 3.0 | 337 | 1.4083 |
| 1.4956 | 3.98 | 448 | 1.4052 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.38.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0 |