transformers pytorch bert text-classification generated_from_trainer dataset:financial_phrasebank base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-uncased
datasets:
- financial_phrasebank
license: apache-2.0
metrics:
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: phrasebank-sentiment-analysis
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: financial_phrasebank
      type: financial_phrasebank
      config: sentences_50agree
      split: train
      args: sentences_50agree
    metrics:
    - type: f1
      value: 0.8307454389992671
      name: F1
    - type: accuracy
      value: 0.8486932599724897
      name: Accuracy
---

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# phrasebank-sentiment-analysis

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6139
- F1: 0.8307
- Accuracy: 0.8487

## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.6277        | 0.94  | 100  | 0.4401          | 0.8025 | 0.8336   |
| 0.3042        | 1.89  | 200  | 0.4034          | 0.8350 | 0.8521   |
| 0.1431        | 2.83  | 300  | 0.5302          | 0.8227 | 0.8418   |
| 0.0642        | 3.77  | 400  | 0.6139          | 0.8307 | 0.8487   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
