End of training

README.md

@@ -15,14 +15,14 @@ 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: 0.6664
-- Answer: {'precision': 0.7112597547380156, 'recall': 0.788627935723115, 'f1': 0.7479484173505275, 'number': 809}
-- Header: {'precision': 0.3125, 'recall': 0.33613445378151263, 'f1': 0.3238866396761134, 'number': 119}
-- Question: {'precision': 0.7686308492201039, 'recall': 0.8328638497652582, 'f1': 0.7994592158630013, 'number': 1065}
-- Overall Precision: 0.7182
+- Loss: 0.6978
+- Answer: {'precision': 0.7016216216216217, 'recall': 0.8022249690976514, 'f1': 0.748558246828143, 'number': 809}
+- Header: {'precision': 0.30327868852459017, 'recall': 0.31092436974789917, 'f1': 0.3070539419087137, 'number': 119}
+- Question: {'precision': 0.7697022767075307, 'recall': 0.8253521126760563, 'f1': 0.7965564114182148, 'number': 1065}
+- Overall Precision: 0.7149
 - Overall Recall: 0.7852
-- Overall F1: 0.7502
-- Overall Accuracy: 0.8137
+- Overall F1: 0.7484
+- Overall Accuracy: 0.7991
 
 ## Model description
 
@@ -52,23 +52,23 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.7752        | 1.0   | 10   | 1.5645          | {'precision': 0.02685765443151298, 'recall': 0.037082818294190356, 'f1': 0.03115264797507788, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.19323308270676692, 'recall': 0.24131455399061033, 'f1': 0.21461377870563672, 'number': 1065} | 0.1173            | 0.1440         | 0.1293     | 0.4207           |
-| 1.4375        | 2.0   | 20   | 1.2207          | {'precision': 0.22950819672131148, 'recall': 0.207663782447466, 'f1': 0.21804023361453603, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.4551231135822081, 'recall': 0.5380281690140845, 'f1': 0.49311531841652323, 'number': 1065}   | 0.3722            | 0.3718         | 0.3720     | 0.6009           |
-| 1.0629        | 3.0   | 30   | 0.9366          | {'precision': 0.5080558539205156, 'recall': 0.584672435105068, 'f1': 0.5436781609195401, 'number': 809}      | {'precision': 0.05263157894736842, 'recall': 0.01680672268907563, 'f1': 0.025477707006369428, 'number': 119} | {'precision': 0.6047700170357752, 'recall': 0.6666666666666666, 'f1': 0.6342117016525234, 'number': 1065}    | 0.5530            | 0.5946         | 0.5730     | 0.7167           |
-| 0.8176        | 4.0   | 40   | 0.7694          | {'precision': 0.6136125654450262, 'recall': 0.7243510506798516, 'f1': 0.6643990929705216, 'number': 809}     | {'precision': 0.23214285714285715, 'recall': 0.1092436974789916, 'f1': 0.14857142857142858, 'number': 119}   | {'precision': 0.6804214223002634, 'recall': 0.7276995305164319, 'f1': 0.7032667876588022, 'number': 1065}    | 0.6391            | 0.6894         | 0.6633     | 0.7641           |
-| 0.6768        | 5.0   | 50   | 0.6961          | {'precision': 0.6569264069264069, 'recall': 0.7503090234857849, 'f1': 0.7005193306405079, 'number': 809}     | {'precision': 0.3023255813953488, 'recall': 0.2184873949579832, 'f1': 0.25365853658536586, 'number': 119}    | {'precision': 0.71733561058924, 'recall': 0.7887323943661971, 'f1': 0.7513416815742396, 'number': 1065}      | 0.6754            | 0.7391         | 0.7058     | 0.7853           |
-| 0.5649        | 6.0   | 60   | 0.6814          | {'precision': 0.6666666666666666, 'recall': 0.7688504326328801, 'f1': 0.7141216991963261, 'number': 809}     | {'precision': 0.26582278481012656, 'recall': 0.17647058823529413, 'f1': 0.2121212121212121, 'number': 119}   | {'precision': 0.6886134779240899, 'recall': 0.8347417840375587, 'f1': 0.7546689303904924, 'number': 1065}    | 0.6652            | 0.7687         | 0.7132     | 0.7948           |
-| 0.4953        | 7.0   | 70   | 0.6521          | {'precision': 0.6859956236323851, 'recall': 0.7750309023485785, 'f1': 0.7278003482298316, 'number': 809}     | {'precision': 0.2616822429906542, 'recall': 0.23529411764705882, 'f1': 0.24778761061946902, 'number': 119}   | {'precision': 0.7305439330543934, 'recall': 0.819718309859155, 'f1': 0.772566371681416, 'number': 1065}      | 0.6895            | 0.7667         | 0.7261     | 0.8031           |
-| 0.4505        | 8.0   | 80   | 0.6362          | {'precision': 0.6862326574172892, 'recall': 0.7948084054388134, 'f1': 0.736540664375716, 'number': 809}      | {'precision': 0.25, 'recall': 0.226890756302521, 'f1': 0.2378854625550661, 'number': 119}                    | {'precision': 0.7349498327759197, 'recall': 0.8253521126760563, 'f1': 0.777532065457762, 'number': 1065}     | 0.6912            | 0.7772         | 0.7317     | 0.8080           |
-| 0.397         | 9.0   | 90   | 0.6430          | {'precision': 0.6900647948164147, 'recall': 0.7898640296662547, 'f1': 0.736599423631124, 'number': 809}      | {'precision': 0.28, 'recall': 0.29411764705882354, 'f1': 0.28688524590163933, 'number': 119}                 | {'precision': 0.753448275862069, 'recall': 0.8206572769953052, 'f1': 0.7856179775280899, 'number': 1065}     | 0.7001            | 0.7767         | 0.7364     | 0.8049           |
-| 0.3588        | 10.0  | 100  | 0.6462          | {'precision': 0.7008830022075055, 'recall': 0.7849196538936959, 'f1': 0.7405247813411079, 'number': 809}     | {'precision': 0.2868217054263566, 'recall': 0.31092436974789917, 'f1': 0.2983870967741935, 'number': 119}    | {'precision': 0.7519247219846023, 'recall': 0.8253521126760563, 'f1': 0.7869292748433303, 'number': 1065}    | 0.7037            | 0.7782         | 0.7391     | 0.8104           |
-| 0.3204        | 11.0  | 110  | 0.6551          | {'precision': 0.7098901098901099, 'recall': 0.7985166872682324, 'f1': 0.7515997673065736, 'number': 809}     | {'precision': 0.3125, 'recall': 0.29411764705882354, 'f1': 0.30303030303030304, 'number': 119}               | {'precision': 0.7634782608695653, 'recall': 0.8244131455399061, 'f1': 0.7927765237020317, 'number': 1065}    | 0.7178            | 0.7822         | 0.7486     | 0.8087           |
-| 0.306         | 12.0  | 120  | 0.6609          | {'precision': 0.7067833698030634, 'recall': 0.7985166872682324, 'f1': 0.7498549042367965, 'number': 809}     | {'precision': 0.3064516129032258, 'recall': 0.31932773109243695, 'f1': 0.31275720164609055, 'number': 119}   | {'precision': 0.7647569444444444, 'recall': 0.8272300469483568, 'f1': 0.7947677041046459, 'number': 1065}    | 0.7146            | 0.7852         | 0.7483     | 0.8091           |
-| 0.2865        | 13.0  | 130  | 0.6623          | {'precision': 0.7144456886898096, 'recall': 0.788627935723115, 'f1': 0.7497062279670975, 'number': 809}      | {'precision': 0.3125, 'recall': 0.33613445378151263, 'f1': 0.3238866396761134, 'number': 119}                | {'precision': 0.7650130548302873, 'recall': 0.8253521126760563, 'f1': 0.7940379403794038, 'number': 1065}    | 0.7175            | 0.7812         | 0.7480     | 0.8137           |
-| 0.2728        | 14.0  | 140  | 0.6639          | {'precision': 0.7112831858407079, 'recall': 0.7948084054388134, 'f1': 0.7507297139521306, 'number': 809}     | {'precision': 0.29133858267716534, 'recall': 0.31092436974789917, 'f1': 0.3008130081300813, 'number': 119}   | {'precision': 0.7649092480553155, 'recall': 0.8309859154929577, 'f1': 0.7965796579657966, 'number': 1065}    | 0.7153            | 0.7852         | 0.7486     | 0.8131           |
-| 0.2747        | 15.0  | 150  | 0.6664          | {'precision': 0.7112597547380156, 'recall': 0.788627935723115, 'f1': 0.7479484173505275, 'number': 809}      | {'precision': 0.3125, 'recall': 0.33613445378151263, 'f1': 0.3238866396761134, 'number': 119}                | {'precision': 0.7686308492201039, 'recall': 0.8328638497652582, 'f1': 0.7994592158630013, 'number': 1065}    | 0.7182            | 0.7852         | 0.7502     | 0.8137           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                                                                        | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8306        | 1.0   | 10   | 1.6241          | {'precision': 0.015978695073235686, 'recall': 0.014833127317676144, 'f1': 0.015384615384615385, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.18994413407821228, 'recall': 0.12769953051643193, 'f1': 0.1527231892195396, 'number': 1065} | 0.1009            | 0.0743         | 0.0855     | 0.3533           |
+| 1.4993        | 2.0   | 20   | 1.2681          | {'precision': 0.11957950065703023, 'recall': 0.11248454882571075, 'f1': 0.11592356687898088, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.43696829079659705, 'recall': 0.5305164319248826, 'f1': 0.4792196776929601, 'number': 1065}  | 0.3192            | 0.3292         | 0.3241     | 0.5668           |
+| 1.1217        | 3.0   | 30   | 0.9584          | {'precision': 0.4690157958687728, 'recall': 0.47713226205191595, 'f1': 0.47303921568627455, 'number': 809}     | {'precision': 0.08333333333333333, 'recall': 0.025210084033613446, 'f1': 0.038709677419354833, 'number': 119} | {'precision': 0.6071741032370953, 'recall': 0.6516431924882629, 'f1': 0.6286231884057971, 'number': 1065}   | 0.5410            | 0.5434         | 0.5422     | 0.7000           |
+| 0.8403        | 4.0   | 40   | 0.7788          | {'precision': 0.6163265306122448, 'recall': 0.7466007416563659, 'f1': 0.6752375628842929, 'number': 809}       | {'precision': 0.2127659574468085, 'recall': 0.08403361344537816, 'f1': 0.12048192771084337, 'number': 119}    | {'precision': 0.6675603217158177, 'recall': 0.7014084507042253, 'f1': 0.6840659340659341, 'number': 1065}   | 0.6342            | 0.6829         | 0.6576     | 0.7495           |
+| 0.6807        | 5.0   | 50   | 0.7110          | {'precision': 0.6525871172122492, 'recall': 0.7639060568603214, 'f1': 0.7038724373576309, 'number': 809}       | {'precision': 0.26865671641791045, 'recall': 0.15126050420168066, 'f1': 0.19354838709677416, 'number': 119}   | {'precision': 0.7077059344552702, 'recall': 0.7502347417840376, 'f1': 0.7283500455788514, 'number': 1065}   | 0.6696            | 0.7200         | 0.6939     | 0.7799           |
+| 0.5615        | 6.0   | 60   | 0.6839          | {'precision': 0.6663135593220338, 'recall': 0.7775030902348579, 'f1': 0.7176269252709641, 'number': 809}       | {'precision': 0.3225806451612903, 'recall': 0.16806722689075632, 'f1': 0.22099447513812157, 'number': 119}    | {'precision': 0.7101214574898785, 'recall': 0.8234741784037559, 'f1': 0.7626086956521739, 'number': 1065}   | 0.6809            | 0.7657         | 0.7208     | 0.7886           |
+| 0.4954        | 7.0   | 70   | 0.6647          | {'precision': 0.6813304721030042, 'recall': 0.7849196538936959, 'f1': 0.7294658242389431, 'number': 809}       | {'precision': 0.28865979381443296, 'recall': 0.23529411764705882, 'f1': 0.2592592592592593, 'number': 119}    | {'precision': 0.7263681592039801, 'recall': 0.8225352112676056, 'f1': 0.7714663143989432, 'number': 1065}   | 0.6886            | 0.7722         | 0.7280     | 0.7957           |
+| 0.4479        | 8.0   | 80   | 0.6529          | {'precision': 0.6748663101604279, 'recall': 0.7799752781211372, 'f1': 0.7236238532110092, 'number': 809}       | {'precision': 0.25742574257425743, 'recall': 0.2184873949579832, 'f1': 0.23636363636363636, 'number': 119}    | {'precision': 0.740016992353441, 'recall': 0.8178403755868544, 'f1': 0.7769848349687779, 'number': 1065}    | 0.6905            | 0.7667         | 0.7266     | 0.8053           |
+| 0.3961        | 9.0   | 90   | 0.6535          | {'precision': 0.6924754634678298, 'recall': 0.7849196538936959, 'f1': 0.7358053302433372, 'number': 809}       | {'precision': 0.25217391304347825, 'recall': 0.24369747899159663, 'f1': 0.24786324786324784, 'number': 119}   | {'precision': 0.7459505541346974, 'recall': 0.8215962441314554, 'f1': 0.7819481680071492, 'number': 1065}   | 0.6980            | 0.7722         | 0.7332     | 0.8020           |
+| 0.3516        | 10.0  | 100  | 0.6645          | {'precision': 0.6899141630901288, 'recall': 0.7948084054388134, 'f1': 0.7386559448592762, 'number': 809}       | {'precision': 0.2857142857142857, 'recall': 0.25210084033613445, 'f1': 0.26785714285714285, 'number': 119}    | {'precision': 0.7667814113597247, 'recall': 0.8366197183098592, 'f1': 0.8001796138302649, 'number': 1065}   | 0.7112            | 0.7847         | 0.7462     | 0.8046           |
+| 0.3197        | 11.0  | 110  | 0.6868          | {'precision': 0.6927194860813705, 'recall': 0.799752781211372, 'f1': 0.7423981640849111, 'number': 809}        | {'precision': 0.2966101694915254, 'recall': 0.29411764705882354, 'f1': 0.2953586497890296, 'number': 119}     | {'precision': 0.7722513089005235, 'recall': 0.8309859154929577, 'f1': 0.8005427408412483, 'number': 1065}   | 0.7129            | 0.7863         | 0.7478     | 0.7996           |
+| 0.2986        | 12.0  | 120  | 0.6912          | {'precision': 0.6914778856526429, 'recall': 0.792336217552534, 'f1': 0.7384792626728109, 'number': 809}        | {'precision': 0.3162393162393162, 'recall': 0.31092436974789917, 'f1': 0.3135593220338983, 'number': 119}     | {'precision': 0.7845057880676759, 'recall': 0.8272300469483568, 'f1': 0.8053016453382084, 'number': 1065}   | 0.7194            | 0.7822         | 0.7495     | 0.7971           |
+| 0.2882        | 13.0  | 130  | 0.7016          | {'precision': 0.6961748633879782, 'recall': 0.7873918417799752, 'f1': 0.7389791183294663, 'number': 809}       | {'precision': 0.3076923076923077, 'recall': 0.3025210084033613, 'f1': 0.30508474576271183, 'number': 119}     | {'precision': 0.7696969696969697, 'recall': 0.8347417840375587, 'f1': 0.8009009009009008, 'number': 1065}   | 0.7142            | 0.7837         | 0.7474     | 0.8028           |
+| 0.2789        | 14.0  | 140  | 0.6994          | {'precision': 0.6989247311827957, 'recall': 0.8034610630407911, 'f1': 0.747556066705003, 'number': 809}        | {'precision': 0.30327868852459017, 'recall': 0.31092436974789917, 'f1': 0.3070539419087137, 'number': 119}    | {'precision': 0.7698343504795118, 'recall': 0.8291079812206573, 'f1': 0.7983725135623869, 'number': 1065}   | 0.7140            | 0.7878         | 0.7490     | 0.7990           |
+| 0.274         | 15.0  | 150  | 0.6978          | {'precision': 0.7016216216216217, 'recall': 0.8022249690976514, 'f1': 0.748558246828143, 'number': 809}        | {'precision': 0.30327868852459017, 'recall': 0.31092436974789917, 'f1': 0.3070539419087137, 'number': 119}    | {'precision': 0.7697022767075307, 'recall': 0.8253521126760563, 'f1': 0.7965564114182148, 'number': 1065}   | 0.7149            | 0.7852         | 0.7484     | 0.7991           |
 
 
 ### Framework versions

logs/events.out.tfevents.1682459035.e3fb78822401.309.2

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+size 14372

pytorch_model.bin

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