transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:financial_phrasebank model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- financial_phrasebank
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: sentiment_bert
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: financial_phrasebank
      type: financial_phrasebank
      config: sentences_66agree
      split: train
      args: sentences_66agree
    metrics:
    - type: accuracy
      value: 0.9360189573459715
      name: Accuracy
---

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# sentiment_bert

This model is a fine-tuned version of [SALT-NLP/FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT) on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3754
- Accuracy: 0.9360

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results



### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
