Update README.md

README.md

@@ -5,40 +5,80 @@ tags:
 model-index:
 - name: bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd
   results: []
+language:
+- en
+metrics:
+- seqeval
+- f1
+- accuracy
+- 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-DFKI-SLT_few-NERd
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None 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: 0.1312
-- Erson: {'precision': 0.8860048426150121, 'recall': 0.9401849948612538, 'f1': 0.912291199202194, 'number': 29190}
-- Ocation: {'precision': 0.8686381704207632, 'recall': 0.8152889539136796, 'f1': 0.841118472477534, 'number': 95690}
-- Rganization: {'precision': 0.7919078915181266, 'recall': 0.7449641777764141, 'f1': 0.7677190874452579, 'number': 65183}
-- Roduct: {'precision': 0.7065968977761166, 'recall': 0.8295304958315051, 'f1': 0.7631446160056513, 'number': 9116}
-- Rt: {'precision': 0.8407258064516129, 'recall': 0.8614333386302241, 'f1': 0.8509536143159878, 'number': 6293}
-- Ther: {'precision': 0.7303024586555996, 'recall': 0.8314124132006586, 'f1': 0.7775843599357258, 'number': 13969}
-- Uilding: {'precision': 0.5162234691388143, 'recall': 0.3648904983617865, 'f1': 0.4275611234592847, 'number': 5799}
-- Vent: {'precision': 0.605920892987139, 'recall': 0.35144264602392683, 'f1': 0.44486014608943525, 'number': 7105}
-- Overall Precision: 0.8203
-- Overall Recall: 0.7886
-- Overall F1: 0.8041
-- Overall Accuracy: 0.9498
+- Person
+  - Precision: 0.8860048426150121
+  - Recall: 0.9401849948612538
+  - F1: 0.912291199202194
+  - Number': 29190
+- Location
+  - Precision: 0.8686381704207632
+  - Recall: 0.8152889539136796
+  - F1: 0.841118472477534
+  - Number: 95690
+- Organization
+  - Precision: 0.7919078915181266
+  - Recall': 0.7449641777764141
+  - F1': 0.7677190874452579
+  - Number': 65183
+- Product
+  - Precision: 0.7065968977761166
+  - Recall: 0.8295304958315051
+  - F1: 0.7631446160056513
+  - Number: 9116
+- Art
+  - Precision: 0.8407258064516129
+  - Recall: 0.8614333386302241
+  - F1: 0.8509536143159878
+  - Number: 6293
+- Other
+  - Precision: 0.7303024586555996
+  - Recall: 0.8314124132006586
+  - F1: 0.7775843599357258
+  - Nnumber: 13969
+- Building
+  - Precision: 0.5162234691388143
+  - Recall: 0.3648904983617865
+  - F1: 0.4275611234592847
+  - Number: 5799
+- Event
+  - Precision: 0.605920892987139
+  - Recall: 0.35144264602392683
+  - F1: 0.44486014608943525
+  - Number': 7105
+- Overall
+  - Precision: 0.8203
+  - Recall: 0.7886
+  - F1: 0.8041
+  - Accuracy: 0.9498
 
 ## Model description
 
-More information needed
+For more information on how it was created, check out the following link: 
 
 ## 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/DFKI-SLT/few-nerd
 
 ## Training procedure
 
@@ -57,15 +97,14 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Erson                                                                                                      | Ocation                                                                                                    | Rganization                                                                                                | Roduct                                                                                                    | Rt                                                                                                        | Ther                                                                                                       | Uilding                                                                                                   | Vent                                                                                                       | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:-----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.1796        | 1.0   | 11293 | 0.1427          | {'precision': 0.8740795762821341, 'recall': 0.9272010962658445, 'f1': 0.8998570336137248, 'number': 29190} | {'precision': 0.8576076215009827, 'recall': 0.8071585327620441, 'f1': 0.8316186723086282, 'number': 95690} | {'precision': 0.7699032109387003, 'recall': 0.7688047497046776, 'f1': 0.7693535882339395, 'number': 65183} | {'precision': 0.6710836277974087, 'recall': 0.75, 'f1': 0.7083506009117282, 'number': 9116}               | {'precision': 0.834716121685375, 'recall': 0.8153503893214683, 'f1': 0.8249196141479099, 'number': 6293}  | {'precision': 0.6742843680056544, 'recall': 0.8195289569761615, 'f1': 0.7398455423789058, 'number': 13969} | {'precision': 0.4812014282713716, 'recall': 0.3950681151922745, 'f1': 0.4339015151515152, 'number': 5799} | {'precision': 0.5997923695821438, 'recall': 0.32526389866291344, 'f1': 0.4217922978645739, 'number': 7105} | 0.8000            | 0.7852         | 0.7925     | 0.9483           |
-| 0.1542        | 2.0   | 22586 | 0.1312          | {'precision': 0.8860048426150121, 'recall': 0.9401849948612538, 'f1': 0.912291199202194, 'number': 29190}  | {'precision': 0.8686381704207632, 'recall': 0.8152889539136796, 'f1': 0.841118472477534, 'number': 95690}  | {'precision': 0.7919078915181266, 'recall': 0.7449641777764141, 'f1': 0.7677190874452579, 'number': 65183} | {'precision': 0.7065968977761166, 'recall': 0.8295304958315051, 'f1': 0.7631446160056513, 'number': 9116} | {'precision': 0.8407258064516129, 'recall': 0.8614333386302241, 'f1': 0.8509536143159878, 'number': 6293} | {'precision': 0.7303024586555996, 'recall': 0.8314124132006586, 'f1': 0.7775843599357258, 'number': 13969} | {'precision': 0.5162234691388143, 'recall': 0.3648904983617865, 'f1': 0.4275611234592847, 'number': 5799} | {'precision': 0.605920892987139, 'recall': 0.35144264602392683, 'f1': 0.44486014608943525, 'number': 7105} | 0.8203            | 0.7886         | 0.8041     | 0.9498           |
-
+| Training Loss | Epoch | Step | Validation Loss | Person Precision | Person Recall | Person F1 | Person Number | Location Precision | Location Recall | Location F1 | Location Number | Organization Precision | Organization Recall | Organization F1 | Organization Number | Product Precision | Product Recall | Product F1 | Product Number | Art Precision | Art Recall | Art F1 | Art Number | Other Precision | Other Recall | Other F1 | Other Number | Building Precision | Building Recall | Building F1 | Building Number | Event Precision | Event Recall | Event F1 | Event Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
+| 0.1796 | 1.0 | 11293 | 0.1427 | 0.8741 | 0.9272 | 0.8999 | 29190 | 0.8576 | 0.8072 | 0.8316 | 95690 | 0.7699 | 0.7688 | 0.7694 | 65183 | 0.6711 | 0.75 | 0.7084 | 9116 | 0.8347 | 0.8154 | 0.8249 | 6293 | 0.6743 | 0.8195 | 0.7398 | 13969 | 0.4812 | 0.3951 | 0.4339 | 5799 | 0.5998 | 0.3253 | 0.4218 | 7105 | 0.8000 | 0.7852 | 0.7925 | 0.9483 |
+| 0.1542 | 2.0 | 22586 | 0.1312 | 0.8860 | 0.9402 | 0.9123 | 29190 | 0.8686 | 0.8153 | 0.8411 | 95690 | 0.7919 | 0.7450 | 0.7677 | 65183 | 0.7066 | 0.8295 | 0.7631 | 9116 | 0.8407 | 0.8614 | 0.8510 | 6293 | 0.7303 | 0.8314 | 0.7776 | 13969 | 0.5162 | 0.3649 | 0.4276 | 5799 | 0.6059 | 0.3514 | 0.4449 | 7105 | 0.8203 | 0.7886 | 0.8041 | 0.9498 |
 
 ### Framework versions
 
 - Transformers 4.30.2
 - Pytorch 2.0.1+cu118
 - Datasets 2.13.1
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3