End of training

README.md

@@ -14,24 +14,20 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 3.0586
-- Ame: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
-- Anguages: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0}
-- Ducation: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
-- Echnical skills: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
-- Escriptions: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
-- Esignation: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
-- Hone number: {'precision': 0.75, 'recall': 1.0, 'f1': 0.8571428571428571, 'number': 3}
-- Ithub: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
-- Mail: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
-- Ocation: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
-- Ork experience company: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
-- Ork experience role: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
-- Rojects: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
-- Overall Precision: 0.12
-- Overall Recall: 0.1071
-- Overall F1: 0.1132
-- Overall Accuracy: 0.1429
+- Loss: 2.7407
+- Education: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
+- Email: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
+- Github: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0}
+- Location: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
+- Name: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
+- Name : {'precision': 0.2, 'recall': 0.5, 'f1': 0.28571428571428575, 'number': 2}
+- Phone Number: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
+- Soft Skills: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0}
+- Technical Skills: {'precision': 0.2, 'recall': 0.35714285714285715, 'f1': 0.25641025641025644, 'number': 14}
+- Overall Precision: 0.1176
+- Overall Recall: 0.2
+- Overall F1: 0.1481
+- Overall Accuracy: 0.1475
 
 ## Model description
 
@@ -60,16 +56,16 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Ame                                                       | Anguages                                                  | Ducation                                                  | Ear of experience                                         | Echnical skills                                           | Escriptions                                               | Esignation                                                | Hone number                                                                                            | Ithub                                                     | Mail                                                      | Ob                                                        | Ocation                                                   | Ork experience company                                    | Ork experience role                                       | Rojects                                                   | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 3.2249        | 1.0   | 3    | 3.1464          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3}                               | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | 0.0909            | 0.0714         | 0.08       | 0.1071           |
-| 2.9723        | 2.0   | 6    | 3.0839          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | 0.0769                                                    | 0.0714            | 0.0741         | 0.0714     |
-| 2.8482        | 3.0   | 9    | 3.0586          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.75, 'recall': 1.0, 'f1': 0.8571428571428571, 'number': 3}| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | 0.12                                                      | 0.1071                                                    | 0.1132            | 0.1429         |
+| Training Loss | Epoch | Step | Validation Loss | Education                                                 | Email                                                     | Github                                                    | Linkedin                                                  | Location                                                  | Name                                                      | Name                                                                                      | Phone Number                                              | Soft Skills                                               | Technical Skills                                                                                           | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 2.9387        | 1.0   | 2    | 2.8701          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.14285714285714285, 'recall': 0.5, 'f1': 0.22222222222222224, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 14}                                                 | 0.0185            | 0.0333         | 0.0238     | 0.0328           |
+| 2.6716        | 2.0   | 4    | 2.7798          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.2, 'recall': 0.5, 'f1': 0.28571428571428575, 'number': 2}| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}                                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.10526315789473684, 'recall': 0.14285714285714285, 'f1': 0.12121212121212122, 'number': 14}| 0.0612                                                                                                     | 0.1               | 0.0759         | 0.1311     |
+| 2.5524        | 3.0   | 6    | 2.7407          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.2, 'recall': 0.5, 'f1': 0.28571428571428575, 'number': 2}| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}                                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.2, 'recall': 0.35714285714285715, 'f1': 0.25641025641025644, 'number': 14}| 0.1176                                                                                                     | 0.2               | 0.1481         | 0.1475     |
 
 
 ### Framework versions
 
-- Transformers 4.34.1
+- Transformers 4.35.0
 - Pytorch 2.1.0+cu118
 - Datasets 2.14.6
 - Tokenizers 0.14.1

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