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Update README.md
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README.md
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@@ -19,6 +19,25 @@ Our model was fine-tuned for Sentiment Analysis task on _FinancialPhraseBank_ da
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### Training data
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FinancialBERT model was fine-tuned on [Financial PhraseBank](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive).
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### How to use
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Our model can be used thanks to Transformers pipeline for sentiment analysis.
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```python
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### Training data
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FinancialBERT model was fine-tuned on [Financial PhraseBank](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive).
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### Fine-tuning hyper-parameters
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- learning_rate = 2e-5
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- batch_size = 32
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- max_seq_length = 512
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- num_train_epochs = 5
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### Metrics
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The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the test set.
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| relation | precision | recall | f1-score | support |
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| ------------- |:-------------:|:-------------:|:-------------:| -----:|
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| has | 0.7416 | 0.9674 | 0.8396 | 2362 |
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| is in | 0.7813 | 0.7925 | 0.7869 | 2362 |
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| is | 0.8650 | 0.6863 | 0.7653 | 2362 |
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| are | 0.8365 | 0.8493 | 0.8429 | 2362 |
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| x | 0.9515 | 0.8302 | 0.8867 | 2362 |
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| macro avg | 0.8352 | 0.8251 | 0.8243 | 11810 |
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| weighted avg | 0.8352 | 0.8251 | 0.8243 | 11810 |
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### How to use
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Our model can be used thanks to Transformers pipeline for sentiment analysis.
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```python
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