Update README.md

README.md

@@ -2,38 +2,52 @@
 license: apache-2.0
 tags:
 - generated_from_trainer
+- medical
+- science
 datasets:
 - ncbi_disease
 model-index:
 - name: bert-base-cased-finetuned-ner-NCBI_Disease
   results: []
+language:
+- en
+metrics:
+- seqeval
+- f1
+- recall
+- accuracy
+- precision
+pipeline_tag: token-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. -->
-
 # bert-base-cased-finetuned-ner-NCBI_Disease
 
 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ncbi_disease dataset.
+
 It achieves the following results on the evaluation set:
 - Loss: 0.0614
-- Disease: {'precision': 0.8063891577928364, 'recall': 0.8677083333333333, 'f1': 0.8359257400903161, 'number': 960}
-- Overall Precision: 0.8064
-- Overall Recall: 0.8677
-- Overall F1: 0.8359
-- Overall Accuracy: 0.9825
+- Disease:
+  - Precision: 0.8063891577928364
+  - Recall: 0.8677083333333333
+  - F1: 0.8359257400903161
+  - Number: 960
+- Overall
+  - Precision: 0.8064
+  - Recall: 0.8677
+  - F1: 0.8359
+  - Accuracy: 0.9825
 
 ## 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/Token%20Classification/Monolingual/NCBI_Disease/NER%20Project%20Using%20NCBI_Disease%20Dataset.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
+Data Source: https://huggingface.co/datasets/ncbi_disease
 
 ## Training procedure
 
@@ -50,16 +64,17 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Disease                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.0525        | 1.0   | 340  | 0.0617          | {'precision': 0.7813471502590673, 'recall': 0.7854166666666667, 'f1': 0.7833766233766233, 'number': 960} | 0.7813            | 0.7854         | 0.7834     | 0.9796           |
-| 0.022         | 2.0   | 680  | 0.0551          | {'precision': 0.7897240723120837, 'recall': 0.8645833333333334, 'f1': 0.8254599701640976, 'number': 960} | 0.7897            | 0.8646         | 0.8255     | 0.9819           |
-| 0.0154        | 3.0   | 1020 | 0.0614          | {'precision': 0.8063891577928364, 'recall': 0.8677083333333333, 'f1': 0.8359257400903161, 'number': 960} | 0.8064            | 0.8677         | 0.8359     | 0.9825           |
+| Training Loss | Epoch | Step | Validation Loss | Disease Precision | Disease Recall | Disease F1 | Disease Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-----------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:--------:|:-----------------:|:--------------:|:----------:|:-------:|
+| 0.0525      | 1.0   | 340  | 0.0617      | 0.7813 | 0.7854 | 0.7834 | 960 | 0.7813 | 0.7854 | 0.7834 | 0.9796 |
+| 0.022       | 2.0   | 680  | 0.0551      | 0.7897 | 0.8646 | 0.8255 | 960 | 0.7897 | 0.8646 | 0.8255 | 0.9819 |
+| 0.0154      | 3.0   | 1020 | 0.0614      | 0.8064 | 0.8677 | 0.8359 | 960 | 0.8064 | 0.8677 | 0.8359 | 0.9825 |
 
+* All values in the above chart are rounded to the nearest ten-thousandth.
 
 ### Framework versions
 
 - Transformers 4.28.1
 - Pytorch 2.0.0
 - Datasets 2.11.0
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3