Update README.md

README.md

@@ -19,6 +19,25 @@ Our model was fine-tuned for Sentiment Analysis task on _FinancialPhraseBank_ da
 ### Training data
 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).
 
+### Fine-tuning hyper-parameters
+- learning_rate = 2e-5
+- batch_size = 32
+- max_seq_length = 512
+- num_train_epochs = 5
+
+### Metrics
+The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the test set.
+| relation  | precision        | recall           | f1-score  | support  |
+| ------------- |:-------------:|:-------------:|:-------------:| -----:|
+| has | 0.7416      | 0.9674 | 0.8396 | 2362 |
+| is in | 0.7813      | 0.7925 | 0.7869 | 2362 |
+| is | 0.8650     | 0.6863 | 0.7653 | 2362 |
+| are | 0.8365     | 0.8493 | 0.8429 | 2362 |
+| x | 0.9515     | 0.8302 | 0.8867 | 2362 |
+|  |      |  |  |  |
+| macro avg | 0.8352     | 0.8251 | 0.8243 | 11810 |
+| weighted avg | 0.8352     | 0.8251 | 0.8243 | 11810 |
+
  ### How to use 
 Our model can be used thanks to Transformers pipeline for sentiment analysis.
 ```python