Update README.md

README.md

@@ -2,42 +2,71 @@
 license: apache-2.0
 tags:
 - generated_from_trainer
+- biology
 datasets:
 - bionlp2004
 model-index:
 - name: bert-base-cased-finetuned-ner-bio_nlp_2004
   results: []
+language:
+- en
+metrics:
+- seqeval
+- f1
+- recall
+- 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-bio_nlp_2004
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the bionlp2004 dataset.
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased).
+
 It achieves the following results on the evaluation set:
 - Loss: 0.2066
-- Dna: {'precision': 0.6619127516778524, 'recall': 0.7471590909090909, 'f1': 0.7019572953736656, 'number': 1056}
-- Rna: {'precision': 0.589041095890411, 'recall': 0.7288135593220338, 'f1': 0.6515151515151515, 'number': 118}
-- Cell Line: {'precision': 0.4758522727272727, 'recall': 0.67, 'f1': 0.5564784053156145, 'number': 500}
-- Cell Type: {'precision': 0.7294117647058823, 'recall': 0.7100468505986466, 'f1': 0.7195990503824848, 'number': 1921}
-- Protein: {'precision': 0.6657656225155033, 'recall': 0.8263272153147819, 'f1': 0.7374075378654457, 'number': 5067}
-- Overall Precision: 0.6628
-- Overall Recall: 0.7805
-- Overall F1: 0.7169
-- Overall Accuracy: 0.9367
+- Dna:
+  - Precision: 0.6619127516778524
+  - Recall: 0.7471590909090909
+  - F1: 0.7019572953736656
+  - Number: 1056
+- Rna:
+  - Precision: 0.589041095890411
+  - Recall: 0.7288135593220338
+  - F1: 0.6515151515151515
+  - Number': 118
+- Cell Line:
+  - Precision: 0.4758522727272727
+  - Recall: 0.67
+  - F1: 0.5564784053156145
+  - Number: 500
+- Cell Type:
+  - Precision: 0.7294117647058823
+  - Recall: 0.7100468505986466
+  - F1: 0.7195990503824848
+  - Number: 1921
+- Protein:
+  - Precision: 0.6657656225155033
+  - Recall: 0.8263272153147819
+  - F1: 0.7374075378654457
+  - Number': 5067
+
+- Overall
+  - Precision: 0.6628
+  - Recall: 0.7805
+  - F1: 0.7169
+  - Accuracy: 0.9367
 
 ## 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/tner-bionlp2004/NER%20Project%20Using%20tner-bionlp%202004%20Dataset%20(BERT-Base).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 Source: https://huggingface.co/datasets/tner/bionlp2004
 
 ## Training procedure
 
@@ -53,17 +82,17 @@ The following hyperparameters were used during training:
 - num_epochs: 3
 
 ### Training results
+| Training Loss | Epoch | Step | Valid. Loss | Dna Precision | Dna Recall | Dna F1 | Dna Number | Rna Precision | Rna Recall | Rna F1 | Rna Number | Cell Line Precision | Cell Type Recall | Cell Type F1 | Cell Type Number | Cell Type | Protein Precision | Protein Recall | Protein F1 | Protein Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:---------:|:-----:|:----:|:---------:|:-------:|:------:|:------:|:------:|:-------------:|:----------:|:------:|:----------:|:----------:|:---------:|:-------:|:-------:|:---------:|:-------:|:--------:|:------:|:-----------:|:--------:|:--------:|:----------:|:---------:|
+| 0.1701 | 1.0 | 1039 | 0.1927 | 0.6153 | 0.7254 | 0.6658 | 1056 | 0.6617 | 0.7458 | 0.7012 | 118 | 0.4670 | 0.608 | 0.5282 | 500 | 0.6997 | 0.7158 | 0.7077 | 1921 | 0.6603 | 0.7833 | 0.7166 |  5067 | 0.6499 | 0.7506 | 0.6966 | 0.9352 |
+| 0.145 | 2.0 | 2078 | 0.1981 | 0.6364 | 0.7443 | 0.6862 | 1056 | 0.6408 | 0.7712 | 0.7000 | 118 | 0.4607 | 0.668 | 0.5453 | 500 | 0.7376 | 0.7022 | 0.7195 | 1921 | 0.6759 | 0.8149 | 0.7389 | 5067 | 0.6662 | 0.7722 | 0.7153 | 0.9364 |
+| 0.1116 | 3.0 | 3117 | 0.2066 | 0.6619 | 0.7472  | 0.7020 | 1056 | 0.5890  | 0.7288 | 0.6515 | 118 | 0.4759 | 0.67 | 0.5565 | 500 | 0.7294 | 0.7100 | 0.7196 | 1921 | 0.6658 | 0.8263 | 0.7374 | 5067 | 0.6628 | 0.7805 | 0.7169 | 0.9367 |
 
-| Training Loss | Epoch | Step | Validation Loss | Dna                                                                                                       | Rna                                                                                                      | Cell Line                                                                                    | Cell Type                                                                                                 | Protein                                                                                                   | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.1701        | 1.0   | 1039 | 0.1927          | {'precision': 0.6152610441767068, 'recall': 0.7253787878787878, 'f1': 0.6657974793568013, 'number': 1056} | {'precision': 0.6616541353383458, 'recall': 0.7457627118644068, 'f1': 0.7011952191235059, 'number': 118} | {'precision': 0.46697388632872505, 'recall': 0.608, 'f1': 0.5282363162467419, 'number': 500} | {'precision': 0.6997455470737913, 'recall': 0.7157730348776679, 'f1': 0.7076685537828101, 'number': 1921} | {'precision': 0.6602894693062719, 'recall': 0.783303730017762, 'f1': 0.716555334897996, 'number': 5067}   | 0.6499            | 0.7506         | 0.6966     | 0.9352           |
-| 0.145         | 2.0   | 2078 | 0.1981          | {'precision': 0.6364372469635627, 'recall': 0.7443181818181818, 'f1': 0.6861632474901789, 'number': 1056} | {'precision': 0.6408450704225352, 'recall': 0.7711864406779662, 'f1': 0.7000000000000002, 'number': 118} | {'precision': 0.4606896551724138, 'recall': 0.668, 'f1': 0.5453061224489797, 'number': 500}  | {'precision': 0.7375615090213231, 'recall': 0.7022384174908901, 'f1': 0.7194666666666666, 'number': 1921} | {'precision': 0.6758880340481257, 'recall': 0.8148805999605289, 'f1': 0.7389047959914101, 'number': 5067} | 0.6662            | 0.7722         | 0.7153     | 0.9364           |
-| 0.1116        | 3.0   | 3117 | 0.2066          | {'precision': 0.6619127516778524, 'recall': 0.7471590909090909, 'f1': 0.7019572953736656, 'number': 1056} | {'precision': 0.589041095890411, 'recall': 0.7288135593220338, 'f1': 0.6515151515151515, 'number': 118}  | {'precision': 0.4758522727272727, 'recall': 0.67, 'f1': 0.5564784053156145, 'number': 500}   | {'precision': 0.7294117647058823, 'recall': 0.7100468505986466, 'f1': 0.7195990503824848, 'number': 1921} | {'precision': 0.6657656225155033, 'recall': 0.8263272153147819, 'f1': 0.7374075378654457, 'number': 5067} | 0.6628            | 0.7805         | 0.7169     | 0.9367           |
-
+* Metrics shown above are rounded to the neareset ten-thousandth
 
 ### Framework versions
 
 - Transformers 4.28.1
 - Pytorch 2.0.0
 - Datasets 2.11.0
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3