Update README.md

README.md

@@ -3,17 +3,20 @@ tags:
 - generated_from_trainer
 metrics:
 - accuracy
+- f1
+- recall
+- precision
 model-index:
 - name: squeezebert-uncased-News_About_Gold
   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. -->
-
 # squeezebert-uncased-News_About_Gold
 
-This model is a fine-tuned version of [squeezebert/squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) on the None dataset.
+This model is a fine-tuned version of [squeezebert/squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased).
 It achieves the following results on the evaluation set:
 - Loss: 0.2643
 - Accuracy: 0.9167
@@ -29,15 +32,17 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)/News%20About%20Gold%20-%20Sentiment%20Analysis%20-%20SqueezeBERT%20with%20W%26B.ipynb
+
+This project is part of a comparison of seven (7) transformers. Here is the README page for the comparison: https://github.com/DunnBC22/NLP_Projects/tree/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)
 
 ## 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 Source: https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-in-commodity-market-gold
 
 ## Training procedure
 
@@ -68,4 +73,4 @@ The following hyperparameters were used during training:
 - Transformers 4.28.1
 - Pytorch 2.0.0
 - Datasets 2.11.0
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3