---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: hf-bert-finetuning
  results: []
datasets:
- zeroshot/twitter-financial-news-sentiment
language:
- en
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# hf-bert-finetuning

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [twitter-financial-news-sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment) dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2672
- Accuracy: 0.805

## Model description

The base model is [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased). It was fine-tuned to perform ternary classification (bullish/neutral/bearish) on financial tweets.

## Intended uses & limitations

This model is intended to be used for demonstrating how to fine-tune a BERT model using the HuggingFace API. The outputs from the model are not meant to be used in a real production use-case (e.g. to classify whether a tweet is bearish or bullish).

## Training and evaluation data

The training and evaluation dataset were taken from the [twitter-financial-news-sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment) dataset on HuggingFace.

## Training procedure

100 training and evaluation examples were randomly sampled from the dataset. This was used to train the BERT model for 100 epochs.

### 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: 100

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.392         | 4.0   | 500   | 1.1767          | 0.805    |
| 0.0421        | 8.0   | 1000  | 1.3555          | 0.814    |
| 0.0266        | 12.0  | 1500  | 1.7734          | 0.806    |
| 0.0066        | 16.0  | 2000  | 1.6149          | 0.818    |
| 0.0264        | 20.0  | 2500  | 1.4583          | 0.823    |
| 0.0284        | 24.0  | 3000  | 1.8117          | 0.794    |
| 0.0019        | 28.0  | 3500  | 1.8569          | 0.804    |
| 0.0336        | 32.0  | 4000  | 1.8200          | 0.801    |
| 0.0221        | 36.0  | 4500  | 1.8082          | 0.806    |
| 0.0195        | 40.0  | 5000  | 1.8102          | 0.81     |
| 0.007         | 44.0  | 5500  | 1.9712          | 0.82     |
| 0.0028        | 48.0  | 6000  | 1.8803          | 0.818    |
| 0.0017        | 52.0  | 6500  | 1.9739          | 0.82     |
| 0.0           | 56.0  | 7000  | 2.0171          | 0.821    |
| 0.019         | 60.0  | 7500  | 1.9017          | 0.805    |
| 0.0           | 64.0  | 8000  | 2.0914          | 0.801    |
| 0.0           | 68.0  | 8500  | 2.1453          | 0.799    |
| 0.0061        | 72.0  | 9000  | 2.2067          | 0.786    |
| 0.0009        | 76.0  | 9500  | 2.1612          | 0.799    |
| 0.0026        | 80.0  | 10000 | 2.1481          | 0.807    |
| 0.0           | 84.0  | 10500 | 2.1813          | 0.807    |
| 0.0           | 88.0  | 11000 | 2.2069          | 0.807    |
| 0.0           | 92.0  | 11500 | 2.2285          | 0.807    |
| 0.0           | 96.0  | 12000 | 2.2422          | 0.807    |
| 0.0004        | 100.0 | 12500 | 2.2672          | 0.805    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.3.0
- Datasets 2.19.0
- Tokenizers 0.19.1