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# financial-sentiment-analysis

This model is a fine-tuned version of [ahmedrachid/FinancialBERT](https://huggingface.co/ahmedrachid/FinancialBERT) on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0395
- Accuracy: 0.9924
- F1: 0.9924

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results



### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "datasets": ["financial_phrasebank"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "financial-sentiment-analysis", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "financial_phrasebank", "type": "financial_phrasebank", "args": "sentences_allagree"}, "metrics": [{"type": "accuracy", "value": 0.9924242424242424, "name": "Accuracy"}, {"type": "f1", "value": 0.9924242424242424, "name": "F1"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:financial_phrasebank model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-financial_phrasebank #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 




# financial-sentiment-analysis

This model is a fine-tuned version of ahmedrachid/FinancialBERT on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0395
- Accuracy: 0.9924
- F1: 0.9924

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results



### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1

# financial-sentiment-analysis

This model is a fine-tuned version of ahmedrachid/FinancialBERT on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0395
- Accuracy: 0.9924
- F1: 0.9924 ## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Training results ### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-financial_phrasebank #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # financial-sentiment-analysis

This model is a fine-tuned version of ahmedrachid/FinancialBERT on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0395
- Accuracy: 0.9924
- F1: 0.9924 ## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Training results ### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-financial_phrasebank #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
# financial-sentiment-analysis

This model is a fine-tuned version of ahmedrachid/FinancialBERT on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0395
- Accuracy: 0.9924
- F1: 0.9924## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5### Training results### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1