sachin18
/

layoutlm-funsd

@@ -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: 0.6806
-- Answer: {'precision': 0.709211986681465, 'recall': 0.7898640296662547, 'f1': 0.7473684210526316, 'number': 809}
-- Header: {'precision': 0.35537190082644626, 'recall': 0.36134453781512604, 'f1': 0.3583333333333333, 'number': 119}
-- Question: {'precision': 0.7920792079207921, 'recall': 0.8262910798122066, 'f1': 0.8088235294117647, 'number': 1065}
-- Overall Precision: 0.7323
-- Overall Recall: 0.7837
-- Overall F1: 0.7571
-- Overall Accuracy: 0.8125
 ## Model description
@@ -54,23 +54,23 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.7526        | 1.0   | 10   | 1.5590          | {'precision': 0.032426778242677826, 'recall': 0.038318912237330034, 'f1': 0.03512747875354107, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.23852295409181637, 'recall': 0.2244131455399061, 'f1': 0.2312530237058539, 'number': 1065} | 0.1379            | 0.1355         | 0.1367     | 0.3812           |
-| 1.4179        | 2.0   | 20   | 1.2477          | {'precision': 0.16770186335403728, 'recall': 0.1668726823238566, 'f1': 0.16728624535315983, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4325309992706054, 'recall': 0.5568075117370892, 'f1': 0.486863711001642, 'number': 1065}   | 0.3343            | 0.3653         | 0.3491     | 0.5813           |
-| 1.0864        | 3.0   | 30   | 0.9440          | {'precision': 0.5470383275261324, 'recall': 0.5822002472187886, 'f1': 0.5640718562874251, 'number': 809}      | {'precision': 0.0425531914893617, 'recall': 0.01680672268907563, 'f1': 0.024096385542168672, 'number': 119} | {'precision': 0.5717665615141956, 'recall': 0.6807511737089202, 'f1': 0.6215173596228033, 'number': 1065}  | 0.5506            | 0.6011         | 0.5747     | 0.7225           |
-| 0.8353        | 4.0   | 40   | 0.7733          | {'precision': 0.5964360587002097, 'recall': 0.7033374536464772, 'f1': 0.6454906409529211, 'number': 809}      | {'precision': 0.19718309859154928, 'recall': 0.11764705882352941, 'f1': 0.14736842105263157, 'number': 119} | {'precision': 0.654468085106383, 'recall': 0.7220657276995305, 'f1': 0.6866071428571429, 'number': 1065}   | 0.6145            | 0.6784         | 0.6449     | 0.7634           |
-| 0.6716        | 5.0   | 50   | 0.7154          | {'precision': 0.6294691224268689, 'recall': 0.7181705809641533, 'f1': 0.6709006928406466, 'number': 809}      | {'precision': 0.24210526315789474, 'recall': 0.19327731092436976, 'f1': 0.2149532710280374, 'number': 119}  | {'precision': 0.6755663430420712, 'recall': 0.784037558685446, 'f1': 0.7257714037375055, 'number': 1065}   | 0.6384            | 0.7220         | 0.6777     | 0.7796           |
-| 0.5748        | 6.0   | 60   | 0.6924          | {'precision': 0.6378269617706237, 'recall': 0.7836835599505563, 'f1': 0.7032723239046034, 'number': 809}      | {'precision': 0.3493975903614458, 'recall': 0.24369747899159663, 'f1': 0.2871287128712871, 'number': 119}   | {'precision': 0.7334558823529411, 'recall': 0.7492957746478873, 'f1': 0.7412912215513237, 'number': 1065}  | 0.6748            | 0.7331         | 0.7027     | 0.7798           |
-| 0.5           | 7.0   | 70   | 0.6652          | {'precision': 0.665258711721225, 'recall': 0.7787391841779975, 'f1': 0.7175398633257404, 'number': 809}       | {'precision': 0.2641509433962264, 'recall': 0.23529411764705882, 'f1': 0.24888888888888888, 'number': 119}  | {'precision': 0.7253218884120172, 'recall': 0.7934272300469484, 'f1': 0.7578475336322871, 'number': 1065}  | 0.6776            | 0.7541         | 0.7138     | 0.7942           |
-| 0.4449        | 8.0   | 80   | 0.6592          | {'precision': 0.6754201680672269, 'recall': 0.7948084054388134, 'f1': 0.730266893810335, 'number': 809}       | {'precision': 0.25862068965517243, 'recall': 0.25210084033613445, 'f1': 0.25531914893617025, 'number': 119} | {'precision': 0.7574692442882249, 'recall': 0.8093896713615023, 'f1': 0.7825692237857468, 'number': 1065}  | 0.6958            | 0.7702         | 0.7311     | 0.8050           |
-| 0.3916        | 9.0   | 90   | 0.6470          | {'precision': 0.7090301003344481, 'recall': 0.7861557478368356, 'f1': 0.7456037514654162, 'number': 809}      | {'precision': 0.3157894736842105, 'recall': 0.3025210084033613, 'f1': 0.30901287553648066, 'number': 119}   | {'precision': 0.762071992976295, 'recall': 0.8150234741784037, 'f1': 0.7876588021778583, 'number': 1065}   | 0.7163            | 0.7727         | 0.7434     | 0.8102           |
-| 0.3807        | 10.0  | 100  | 0.6552          | {'precision': 0.6869009584664537, 'recall': 0.7972805933250927, 'f1': 0.7379862700228833, 'number': 809}      | {'precision': 0.2972972972972973, 'recall': 0.2773109243697479, 'f1': 0.28695652173913044, 'number': 119}   | {'precision': 0.7832422586520947, 'recall': 0.8075117370892019, 'f1': 0.7951918631530283, 'number': 1065}  | 0.7160            | 0.7717         | 0.7428     | 0.8129           |
-| 0.328         | 11.0  | 110  | 0.6710          | {'precision': 0.7014428412874584, 'recall': 0.7812113720642769, 'f1': 0.7391812865497076, 'number': 809}      | {'precision': 0.3037037037037037, 'recall': 0.3445378151260504, 'f1': 0.3228346456692913, 'number': 119}    | {'precision': 0.7671589921807124, 'recall': 0.8291079812206573, 'f1': 0.7969314079422383, 'number': 1065}  | 0.7115            | 0.7807         | 0.7445     | 0.8076           |
-| 0.3111        | 12.0  | 120  | 0.6772          | {'precision': 0.6972972972972973, 'recall': 0.7972805933250927, 'f1': 0.7439446366782007, 'number': 809}      | {'precision': 0.34234234234234234, 'recall': 0.31932773109243695, 'f1': 0.33043478260869563, 'number': 119} | {'precision': 0.801477377654663, 'recall': 0.8150234741784037, 'f1': 0.8081936685288641, 'number': 1065}   | 0.7319            | 0.7782         | 0.7544     | 0.8120           |
-| 0.2936        | 13.0  | 130  | 0.6751          | {'precision': 0.7136563876651982, 'recall': 0.8009888751545118, 'f1': 0.7548048922539313, 'number': 809}      | {'precision': 0.33858267716535434, 'recall': 0.36134453781512604, 'f1': 0.34959349593495936, 'number': 119} | {'precision': 0.7894736842105263, 'recall': 0.8309859154929577, 'f1': 0.8096980786825252, 'number': 1065}  | 0.7310            | 0.7908         | 0.7597     | 0.8126           |
-| 0.2719        | 14.0  | 140  | 0.6794          | {'precision': 0.7081021087680355, 'recall': 0.788627935723115, 'f1': 0.7461988304093568, 'number': 809}       | {'precision': 0.3524590163934426, 'recall': 0.36134453781512604, 'f1': 0.35684647302904565, 'number': 119}  | {'precision': 0.794755877034358, 'recall': 0.8253521126760563, 'f1': 0.809765085214187, 'number': 1065}    | 0.7327            | 0.7827         | 0.7569     | 0.8116           |
-| 0.2776        | 15.0  | 150  | 0.6806          | {'precision': 0.709211986681465, 'recall': 0.7898640296662547, 'f1': 0.7473684210526316, 'number': 809}       | {'precision': 0.35537190082644626, 'recall': 0.36134453781512604, 'f1': 0.3583333333333333, 'number': 119}  | {'precision': 0.7920792079207921, 'recall': 0.8262910798122066, 'f1': 0.8088235294117647, 'number': 1065}  | 0.7323            | 0.7837         | 0.7571     | 0.8125           |
 ### Framework versions

 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: 0.6746
+- Answer: {'precision': 0.7057569296375267, 'recall': 0.8182941903584673, 'f1': 0.7578706353749285, 'number': 809}
+- Header: {'precision': 0.3508771929824561, 'recall': 0.33613445378151263, 'f1': 0.34334763948497854, 'number': 119}
+- Question: {'precision': 0.7793345008756567, 'recall': 0.8356807511737089, 'f1': 0.8065246941549614, 'number': 1065}
+- Overall Precision: 0.7256
+- Overall Recall: 0.7988
+- Overall F1: 0.7604
+- Overall Accuracy: 0.8085
 ## Model description
 ### Training results
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                                                                        | Question                                                                                                   | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8024        | 1.0   | 10   | 1.6086          | {'precision': 0.009900990099009901, 'recall': 0.007416563658838072, 'f1': 0.008480565371024736, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.20625, 'recall': 0.12394366197183099, 'f1': 0.15483870967741936, 'number': 1065}           | 0.1108            | 0.0692         | 0.0852     | 0.3458           |
+| 1.4593        | 2.0   | 20   | 1.2405          | {'precision': 0.13250283125707815, 'recall': 0.1446229913473424, 'f1': 0.13829787234042556, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.42007168458781363, 'recall': 0.5502347417840375, 'f1': 0.4764227642276423, 'number': 1065} | 0.3086            | 0.3527         | 0.3292     | 0.5822           |
+| 1.1064        | 3.0   | 30   | 0.9251          | {'precision': 0.46214355948869223, 'recall': 0.580964153275649, 'f1': 0.5147864184008761, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.550321199143469, 'recall': 0.723943661971831, 'f1': 0.6253041362530414, 'number': 1065}    | 0.5111            | 0.6227         | 0.5614     | 0.7118           |
+| 0.8548        | 4.0   | 40   | 0.7690          | {'precision': 0.5675413022351797, 'recall': 0.7218788627935723, 'f1': 0.6354733405875952, 'number': 809}       | {'precision': 0.02564102564102564, 'recall': 0.008403361344537815, 'f1': 0.012658227848101267, 'number': 119} | {'precision': 0.6504534212695795, 'recall': 0.7408450704225352, 'f1': 0.6927129060579456, 'number': 1065}  | 0.6024            | 0.6894         | 0.6430     | 0.7602           |
+| 0.6855        | 5.0   | 50   | 0.7230          | {'precision': 0.6310572687224669, 'recall': 0.7082818294190358, 'f1': 0.6674432149097262, 'number': 809}       | {'precision': 0.2054794520547945, 'recall': 0.12605042016806722, 'f1': 0.15625, 'number': 119}                | {'precision': 0.6592818945760123, 'recall': 0.8103286384976526, 'f1': 0.7270429654591406, 'number': 1065}  | 0.6336            | 0.7280         | 0.6776     | 0.7785           |
+| 0.5838        | 6.0   | 60   | 0.6791          | {'precision': 0.6316297010607522, 'recall': 0.8096415327564895, 'f1': 0.7096424702058506, 'number': 809}       | {'precision': 0.25, 'recall': 0.15126050420168066, 'f1': 0.18848167539267013, 'number': 119}                  | {'precision': 0.7356521739130435, 'recall': 0.7943661971830986, 'f1': 0.7638826185101579, 'number': 1065}  | 0.6724            | 0.7622         | 0.7145     | 0.7886           |
+| 0.499         | 7.0   | 70   | 0.6482          | {'precision': 0.6722689075630253, 'recall': 0.7911001236093943, 'f1': 0.7268597387847815, 'number': 809}       | {'precision': 0.30612244897959184, 'recall': 0.25210084033613445, 'f1': 0.2764976958525346, 'number': 119}    | {'precision': 0.7367972742759795, 'recall': 0.812206572769953, 'f1': 0.7726663689146941, 'number': 1065}   | 0.6902            | 0.7702         | 0.7280     | 0.8001           |
+| 0.4429        | 8.0   | 80   | 0.6642          | {'precision': 0.6596596596596597, 'recall': 0.8145859085290482, 'f1': 0.7289823008849557, 'number': 809}       | {'precision': 0.26605504587155965, 'recall': 0.24369747899159663, 'f1': 0.2543859649122807, 'number': 119}    | {'precision': 0.7445193929173693, 'recall': 0.8291079812206573, 'f1': 0.7845402043536206, 'number': 1065}  | 0.6848            | 0.7883         | 0.7329     | 0.7998           |
+| 0.387         | 9.0   | 90   | 0.6536          | {'precision': 0.6892177589852009, 'recall': 0.8059332509270705, 'f1': 0.743019943019943, 'number': 809}        | {'precision': 0.3269230769230769, 'recall': 0.2857142857142857, 'f1': 0.30493273542600896, 'number': 119}     | {'precision': 0.757912745936698, 'recall': 0.831924882629108, 'f1': 0.7931960608773501, 'number': 1065}    | 0.7084            | 0.7888         | 0.7464     | 0.8018           |
+| 0.3798        | 10.0  | 100  | 0.6564          | {'precision': 0.6893305439330544, 'recall': 0.8145859085290482, 'f1': 0.746742209631728, 'number': 809}        | {'precision': 0.3055555555555556, 'recall': 0.2773109243697479, 'f1': 0.2907488986784141, 'number': 119}      | {'precision': 0.7616580310880829, 'recall': 0.828169014084507, 'f1': 0.7935222672064778, 'number': 1065}   | 0.7084            | 0.7898         | 0.7469     | 0.8132           |
+| 0.3185        | 11.0  | 110  | 0.6684          | {'precision': 0.690700104493208, 'recall': 0.8170580964153276, 'f1': 0.7485843714609287, 'number': 809}        | {'precision': 0.3230769230769231, 'recall': 0.35294117647058826, 'f1': 0.3373493975903615, 'number': 119}     | {'precision': 0.761168384879725, 'recall': 0.831924882629108, 'f1': 0.7949753252579633, 'number': 1065}    | 0.7059            | 0.7973         | 0.7488     | 0.8018           |
+| 0.3035        | 12.0  | 120  | 0.6603          | {'precision': 0.69989281886388, 'recall': 0.8071693448702101, 'f1': 0.7497129735935705, 'number': 809}         | {'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119}     | {'precision': 0.7688966116420504, 'recall': 0.8309859154929577, 'f1': 0.7987364620938627, 'number': 1065}  | 0.7173            | 0.7908         | 0.7523     | 0.8129           |
+| 0.2848        | 13.0  | 130  | 0.6748          | {'precision': 0.695697796432319, 'recall': 0.8195302843016069, 'f1': 0.7525539160045404, 'number': 809}        | {'precision': 0.3474576271186441, 'recall': 0.3445378151260504, 'f1': 0.3459915611814346, 'number': 119}      | {'precision': 0.7705061082024433, 'recall': 0.8291079812206573, 'f1': 0.798733604703754, 'number': 1065}   | 0.7158            | 0.7963         | 0.7539     | 0.8063           |
+| 0.2628        | 14.0  | 140  | 0.6744          | {'precision': 0.7089151450053706, 'recall': 0.8158220024721878, 'f1': 0.7586206896551725, 'number': 809}       | {'precision': 0.358974358974359, 'recall': 0.35294117647058826, 'f1': 0.35593220338983056, 'number': 119}     | {'precision': 0.7739965095986039, 'recall': 0.8328638497652582, 'f1': 0.8023518769787427, 'number': 1065}  | 0.7242            | 0.7973         | 0.7590     | 0.8092           |
+| 0.262         | 15.0  | 150  | 0.6746          | {'precision': 0.7057569296375267, 'recall': 0.8182941903584673, 'f1': 0.7578706353749285, 'number': 809}       | {'precision': 0.3508771929824561, 'recall': 0.33613445378151263, 'f1': 0.34334763948497854, 'number': 119}    | {'precision': 0.7793345008756567, 'recall': 0.8356807511737089, 'f1': 0.8065246941549614, 'number': 1065}  | 0.7256            | 0.7988         | 0.7604     | 0.8085           |
 ### Framework versions

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