Update README.md

README.md

@@ -4,17 +4,20 @@ tags:
 - generated_from_trainer
 metrics:
 - accuracy
+- f1
+- recall
+- precision
 model-index:
 - name: fnet-large-Financial_Sentiment_Analysis_v3
   results: []
+language:
+- en
+pipeline_tag: text-classification
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # fnet-large-Financial_Sentiment_Analysis_v3
 
-This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the None dataset.
+This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large).
 It achieves the following results on the evaluation set:
 - Loss: 0.4741
 - Accuracy: 0.8248
@@ -30,15 +33,19 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This is a sentiment analysis (text classification) model concern comments about finances.
+
+https://github.com/DunnBC22/NLP_Projects/blob/main/Sentiment%20Analysis/Financial%20Sentiment%20Analysis/Financial_Sentiment_Analysis_v3.ipynb
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended to demonstrate my ability to solve a complex problem using technology.
 
 ## Training and evaluation data
 
-More information needed
+Dataset Sources:
+- https://www.kaggle.com/datasets/sbhatti/financial-sentiment-analysis
+- https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-for-financial-news
 
 ## Training procedure
 
@@ -74,4 +81,4 @@ The following hyperparameters were used during training:
 - Transformers 4.27.4
 - Pytorch 2.0.0
 - Datasets 2.11.0
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3