Update README.md

README.md

@@ -21,19 +21,22 @@ This model is a fine-tuned version of [google/fnet-large](https://huggingface.co
 It achieves the following results on the evaluation set:
 - Loss: 0.4741
 - Accuracy: 0.8248
-- Weighted f1: 0.8194
-- Micro f1: 0.8248
-- Macro f1: 0.7369
-- Weighted recall: 0.8248
-- Micro recall: 0.8248
-- Macro recall: 0.7269
-- Weighted precision: 0.8163
-- Micro precision: 0.8248
-- Macro precision: 0.7515
+- F1
+  - Weighted: 0.8194
+  - Micro: 0.8248
+  - Macro: 0.7369
+- Recall
+  - Weighted: 0.8248
+  - Micro: 0.8248
+  - Macro: 0.7269
+- Precision
+  - Weighted: 0.8163
+  - Micro: 0.8248
+  - Macro: 0.7515
 
 ## Model description
 
-This is a sentiment analysis (text classification) model concern comments about finances.
+This is a sentiment analysis (text classification) model concerning comments about finances.
 
 For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Sentiment%20Analysis/Financial%20Sentiment%20Analysis/Financial_Sentiment_Analysis_v3.ipynb
 
@@ -62,7 +65,7 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
 | 0.6757        | 1.0   | 134  | 0.5890          | 0.5855   | 0.4739      | 0.5855   | 0.3628   | 0.5855          | 0.5855       | 0.4298       | 0.5912             | 0.5855          | 0.5210          |
 | 0.4815        | 2.0   | 268  | 0.3994          | 0.7827   | 0.7789      | 0.7827   | 0.7156   | 0.7827          | 0.7827       | 0.7039       | 0.7878             | 0.7827          | 0.7388          |