End of training

README.md

@@ -17,14 +17,14 @@ 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 funsd dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6244
-- Answer: {'precision': 0.6850886339937435, 'recall': 0.8121137206427689, 'f1': 0.743212669683258, 'number': 809}
-- Header: {'precision': 0.3118279569892473, 'recall': 0.24369747899159663, 'f1': 0.27358490566037735, 'number': 119}
-- Question: {'precision': 0.7454228421970357, 'recall': 0.8028169014084507, 'f1': 0.7730560578661845, 'number': 1065}
-- Overall Precision: 0.7008
-- Overall Recall: 0.7732
-- Overall F1: 0.7352
-- Overall Accuracy: 0.8082
+- Loss: 0.6712
+- Answer: {'precision': 0.6719409282700421, 'recall': 0.7873918417799752, 'f1': 0.7250996015936254, 'number': 809}
+- Header: {'precision': 0.3153153153153153, 'recall': 0.29411764705882354, 'f1': 0.30434782608695654, 'number': 119}
+- Question: {'precision': 0.7069109075770191, 'recall': 0.7971830985915493, 'f1': 0.7493380406001765, 'number': 1065}
+- Overall Precision: 0.6730
+- Overall Recall: 0.7632
+- Overall F1: 0.7153
+- Overall Accuracy: 0.7909
 
 ## Model description
 
@@ -54,18 +54,18 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                       | Header                                                                                                         | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.815         | 1.0   | 10   | 1.5977          | {'precision': 0.01854714064914992, 'recall': 0.014833127317676144, 'f1': 0.01648351648351648, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.28852459016393445, 'recall': 0.1652582159624413, 'f1': 0.21014925373134327, 'number': 1065} | 0.1496            | 0.0943         | 0.1157     | 0.3533           |
-| 1.4806        | 2.0   | 20   | 1.2889          | {'precision': 0.15517241379310345, 'recall': 0.20024721878862795, 'f1': 0.174851592012952, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.42313218390804597, 'recall': 0.5530516431924882, 'f1': 0.47944647944647945, 'number': 1065} | 0.3083            | 0.3768         | 0.3391     | 0.5762           |
-| 1.1569        | 3.0   | 30   | 0.9771          | {'precision': 0.40138751238850345, 'recall': 0.5006180469715699, 'f1': 0.44554455445544555, 'number': 809}   | {'precision': 0.038461538461538464, 'recall': 0.008403361344537815, 'f1': 0.013793103448275862, 'number': 119} | {'precision': 0.5620767494356659, 'recall': 0.7014084507042253, 'f1': 0.6240601503759399, 'number': 1065}   | 0.4877            | 0.5785         | 0.5293     | 0.6555           |
-| 0.891         | 4.0   | 40   | 0.8126          | {'precision': 0.5368516833484986, 'recall': 0.7292954264524104, 'f1': 0.618448637316562, 'number': 809}      | {'precision': 0.16279069767441862, 'recall': 0.058823529411764705, 'f1': 0.08641975308641975, 'number': 119}   | {'precision': 0.6547202797202797, 'recall': 0.7032863849765258, 'f1': 0.6781349026708917, 'number': 1065}   | 0.5888            | 0.6754         | 0.6291     | 0.7338           |
-| 0.7304        | 5.0   | 50   | 0.7076          | {'precision': 0.6277836691410392, 'recall': 0.7317676143386898, 'f1': 0.6757990867579908, 'number': 809}     | {'precision': 0.22535211267605634, 'recall': 0.13445378151260504, 'f1': 0.16842105263157894, 'number': 119}    | {'precision': 0.6539360872954014, 'recall': 0.787793427230047, 'f1': 0.7146507666098807, 'number': 1065}    | 0.6300            | 0.7260         | 0.6746     | 0.7781           |
-| 0.6363        | 6.0   | 60   | 0.6767          | {'precision': 0.6412825651302605, 'recall': 0.7911001236093943, 'f1': 0.7083563918096293, 'number': 809}     | {'precision': 0.3235294117647059, 'recall': 0.18487394957983194, 'f1': 0.23529411764705885, 'number': 119}     | {'precision': 0.7082969432314411, 'recall': 0.7615023474178404, 'f1': 0.7339366515837105, 'number': 1065}   | 0.6662            | 0.7391         | 0.7008     | 0.7846           |
-| 0.5714        | 7.0   | 70   | 0.6386          | {'precision': 0.6797040169133193, 'recall': 0.7948084054388134, 'f1': 0.7327635327635328, 'number': 809}     | {'precision': 0.313953488372093, 'recall': 0.226890756302521, 'f1': 0.2634146341463415, 'number': 119}         | {'precision': 0.726962457337884, 'recall': 0.8, 'f1': 0.7617344658024139, 'number': 1065}                   | 0.6906            | 0.7637         | 0.7253     | 0.8048           |
-| 0.5241        | 8.0   | 80   | 0.6398          | {'precision': 0.6753112033195021, 'recall': 0.8046971569839307, 'f1': 0.7343485617597293, 'number': 809}     | {'precision': 0.29213483146067415, 'recall': 0.2184873949579832, 'f1': 0.25, 'number': 119}                    | {'precision': 0.7306015693112468, 'recall': 0.7868544600938967, 'f1': 0.7576853526220616, 'number': 1065}   | 0.6886            | 0.7602         | 0.7226     | 0.8005           |
-| 0.4861        | 9.0   | 90   | 0.6272          | {'precision': 0.6785340314136126, 'recall': 0.8009888751545118, 'f1': 0.7346938775510204, 'number': 809}     | {'precision': 0.3010752688172043, 'recall': 0.23529411764705882, 'f1': 0.2641509433962264, 'number': 119}      | {'precision': 0.7407407407407407, 'recall': 0.8075117370892019, 'f1': 0.7726864330637916, 'number': 1065}   | 0.6953            | 0.7707         | 0.7311     | 0.8087           |
-| 0.5004        | 10.0  | 100  | 0.6244          | {'precision': 0.6850886339937435, 'recall': 0.8121137206427689, 'f1': 0.743212669683258, 'number': 809}      | {'precision': 0.3118279569892473, 'recall': 0.24369747899159663, 'f1': 0.27358490566037735, 'number': 119}     | {'precision': 0.7454228421970357, 'recall': 0.8028169014084507, 'f1': 0.7730560578661845, 'number': 1065}   | 0.7008            | 0.7732         | 0.7352     | 0.8082           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                       | Header                                                                                                      | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7528        | 1.0   | 10   | 1.5450          | {'precision': 0.04079497907949791, 'recall': 0.048207663782447466, 'f1': 0.04419263456090652, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.1806060606060606, 'recall': 0.13990610328638498, 'f1': 0.15767195767195766, 'number': 1065} | 0.1056            | 0.0943         | 0.0996     | 0.3786           |
+| 1.4294        | 2.0   | 20   | 1.2643          | {'precision': 0.20842824601366744, 'recall': 0.22620519159456118, 'f1': 0.2169531713100178, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4424778761061947, 'recall': 0.5164319248826291, 'f1': 0.4766031195840555, 'number': 1065}   | 0.3456            | 0.3678         | 0.3563     | 0.5767           |
+| 1.1277        | 3.0   | 30   | 0.9879          | {'precision': 0.4243845252051583, 'recall': 0.44746600741656367, 'f1': 0.4356197352587245, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5726141078838174, 'recall': 0.647887323943662, 'f1': 0.6079295154185022, 'number': 1065}    | 0.5092            | 0.5278         | 0.5184     | 0.6932           |
+| 0.8834        | 4.0   | 40   | 0.8188          | {'precision': 0.574052812858783, 'recall': 0.6180469715698393, 'f1': 0.5952380952380952, 'number': 809}      | {'precision': 0.12, 'recall': 0.05042016806722689, 'f1': 0.07100591715976332, 'number': 119}                | {'precision': 0.6459369817578773, 'recall': 0.7314553990610329, 'f1': 0.6860413914575078, 'number': 1065}   | 0.6041            | 0.6448         | 0.6238     | 0.7497           |
+| 0.7042        | 5.0   | 50   | 0.7333          | {'precision': 0.628385698808234, 'recall': 0.7169344870210136, 'f1': 0.6697459584295612, 'number': 809}      | {'precision': 0.29411764705882354, 'recall': 0.16806722689075632, 'f1': 0.21390374331550802, 'number': 119} | {'precision': 0.6616242038216561, 'recall': 0.780281690140845, 'f1': 0.7160706591986213, 'number': 1065}    | 0.6368            | 0.7180         | 0.6750     | 0.7748           |
+| 0.6134        | 6.0   | 60   | 0.7075          | {'precision': 0.6507276507276507, 'recall': 0.7737948084054388, 'f1': 0.7069452286843592, 'number': 809}     | {'precision': 0.2987012987012987, 'recall': 0.19327731092436976, 'f1': 0.23469387755102045, 'number': 119}  | {'precision': 0.7140366172624237, 'recall': 0.7690140845070422, 'f1': 0.7405063291139241, 'number': 1065}   | 0.6715            | 0.7366         | 0.7026     | 0.7789           |
+| 0.5519        | 7.0   | 70   | 0.6817          | {'precision': 0.6593521421107628, 'recall': 0.7799752781211372, 'f1': 0.7146092865232163, 'number': 809}     | {'precision': 0.3333333333333333, 'recall': 0.24369747899159663, 'f1': 0.2815533980582524, 'number': 119}   | {'precision': 0.7023608768971332, 'recall': 0.7821596244131456, 'f1': 0.7401155042203464, 'number': 1065}   | 0.6695            | 0.7491         | 0.7071     | 0.7857           |
+| 0.5105        | 8.0   | 80   | 0.6738          | {'precision': 0.6628630705394191, 'recall': 0.7898640296662547, 'f1': 0.7208121827411168, 'number': 809}     | {'precision': 0.2912621359223301, 'recall': 0.25210084033613445, 'f1': 0.2702702702702703, 'number': 119}   | {'precision': 0.709106239460371, 'recall': 0.7896713615023474, 'f1': 0.7472234562416704, 'number': 1065}    | 0.6702            | 0.7577         | 0.7113     | 0.7899           |
+| 0.4684        | 9.0   | 90   | 0.6721          | {'precision': 0.6656217345872518, 'recall': 0.7873918417799752, 'f1': 0.7214043035107587, 'number': 809}     | {'precision': 0.3090909090909091, 'recall': 0.2857142857142857, 'f1': 0.296943231441048, 'number': 119}     | {'precision': 0.703150912106136, 'recall': 0.7962441314553991, 'f1': 0.7468075737560547, 'number': 1065}    | 0.6683            | 0.7622         | 0.7121     | 0.7906           |
+| 0.4814        | 10.0  | 100  | 0.6712          | {'precision': 0.6719409282700421, 'recall': 0.7873918417799752, 'f1': 0.7250996015936254, 'number': 809}     | {'precision': 0.3153153153153153, 'recall': 0.29411764705882354, 'f1': 0.30434782608695654, 'number': 119}  | {'precision': 0.7069109075770191, 'recall': 0.7971830985915493, 'f1': 0.7493380406001765, 'number': 1065}   | 0.6730            | 0.7632         | 0.7153     | 0.7909           |
 
 
 ### Framework versions

logs/events.out.tfevents.1720071331.HCIDC-SV-DMZ-ORC-NODE02.1682304.0

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model.safetensors

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