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--- |
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license: apache-2.0 |
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base_model: google-bert/bert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: hf-bert-finetuning |
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results: [] |
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datasets: |
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- zeroshot/twitter-financial-news-sentiment |
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language: |
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- en |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# hf-bert-finetuning |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.2672 |
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- Accuracy: 0.805 |
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## Model description |
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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. |
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## Intended uses & limitations |
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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). |
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## Training and evaluation data |
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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. |
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## Training procedure |
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100 training and evaluation examples were randomly sampled from the dataset. This was used to train the BERT model for 100 epochs. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.392 | 4.0 | 500 | 1.1767 | 0.805 | |
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| 0.0421 | 8.0 | 1000 | 1.3555 | 0.814 | |
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| 0.0266 | 12.0 | 1500 | 1.7734 | 0.806 | |
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| 0.0066 | 16.0 | 2000 | 1.6149 | 0.818 | |
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| 0.0264 | 20.0 | 2500 | 1.4583 | 0.823 | |
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| 0.0284 | 24.0 | 3000 | 1.8117 | 0.794 | |
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| 0.0019 | 28.0 | 3500 | 1.8569 | 0.804 | |
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| 0.0336 | 32.0 | 4000 | 1.8200 | 0.801 | |
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| 0.0221 | 36.0 | 4500 | 1.8082 | 0.806 | |
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| 0.0195 | 40.0 | 5000 | 1.8102 | 0.81 | |
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| 0.007 | 44.0 | 5500 | 1.9712 | 0.82 | |
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| 0.0028 | 48.0 | 6000 | 1.8803 | 0.818 | |
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| 0.0017 | 52.0 | 6500 | 1.9739 | 0.82 | |
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| 0.0 | 56.0 | 7000 | 2.0171 | 0.821 | |
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| 0.019 | 60.0 | 7500 | 1.9017 | 0.805 | |
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| 0.0 | 64.0 | 8000 | 2.0914 | 0.801 | |
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| 0.0 | 68.0 | 8500 | 2.1453 | 0.799 | |
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| 0.0061 | 72.0 | 9000 | 2.2067 | 0.786 | |
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| 0.0009 | 76.0 | 9500 | 2.1612 | 0.799 | |
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| 0.0026 | 80.0 | 10000 | 2.1481 | 0.807 | |
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| 0.0 | 84.0 | 10500 | 2.1813 | 0.807 | |
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| 0.0 | 88.0 | 11000 | 2.2069 | 0.807 | |
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| 0.0 | 92.0 | 11500 | 2.2285 | 0.807 | |
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| 0.0 | 96.0 | 12000 | 2.2422 | 0.807 | |
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| 0.0004 | 100.0 | 12500 | 2.2672 | 0.805 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.3.0 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
