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: 0.6865
-- Answer: {'precision': 0.6990185387131952, 'recall': 0.792336217552534, 'f1': 0.7427578215527232, 'number': 809}
-- Header: {'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119}
-- Question: {'precision': 0.7904085257548845, 'recall': 0.8356807511737089, 'f1': 0.8124144226380648, 'number': 1065}
-- Overall Precision: 0.7268
-- Overall Recall: 0.7888
-- Overall F1: 0.7565
-- Overall Accuracy: 0.8047
+- 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
+- Overall Recall: 0.7852
+- Overall F1: 0.7502
+- Overall Accuracy: 0.8137
 
 ## Model description
 
@@ -52,28 +52,28 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.7784        | 1.0   | 10   | 1.6271          | {'precision': 0.01841620626151013, 'recall': 0.012360939431396786, 'f1': 0.014792899408284023, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.11462450592885376, 'recall': 0.054460093896713614, 'f1': 0.07383831954169319, 'number': 1065} | 0.0648            | 0.0341         | 0.0447     | 0.3258           |
-| 1.4893        | 2.0   | 20   | 1.2865          | {'precision': 0.18452935694315004, 'recall': 0.24474660074165636, 'f1': 0.21041445270988307, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.4293563579277865, 'recall': 0.5136150234741784, 'f1': 0.4677212483967507, 'number': 1065}     | 0.3174            | 0.3738         | 0.3433     | 0.5703           |
-| 1.1173        | 3.0   | 30   | 0.9566          | {'precision': 0.4382845188284519, 'recall': 0.5179233621755254, 'f1': 0.4747875354107649, 'number': 809}      | {'precision': 0.045454545454545456, 'recall': 0.01680672268907563, 'f1': 0.024539877300613498, 'number': 119} | {'precision': 0.5686113393590797, 'recall': 0.6497652582159624, 'f1': 0.6064855390008765, 'number': 1065}     | 0.5020            | 0.5585         | 0.5287     | 0.6883           |
-| 0.8579        | 4.0   | 40   | 0.8042          | {'precision': 0.5834932821497121, 'recall': 0.7515451174289246, 'f1': 0.6569421934089681, 'number': 809}      | {'precision': 0.18055555555555555, 'recall': 0.1092436974789916, 'f1': 0.13612565445026178, 'number': 119}    | {'precision': 0.6401480111008325, 'recall': 0.6497652582159624, 'f1': 0.6449207828518173, 'number': 1065}     | 0.5982            | 0.6588         | 0.6270     | 0.7438           |
-| 0.711         | 5.0   | 50   | 0.7251          | {'precision': 0.6355140186915887, 'recall': 0.7564894932014833, 'f1': 0.6907449209932279, 'number': 809}      | {'precision': 0.25252525252525254, 'recall': 0.21008403361344538, 'f1': 0.22935779816513763, 'number': 119}   | {'precision': 0.6740237691001698, 'recall': 0.7455399061032864, 'f1': 0.7079803834150691, 'number': 1065}     | 0.6388            | 0.7180         | 0.6761     | 0.7764           |
-| 0.5916        | 6.0   | 60   | 0.6914          | {'precision': 0.6471204188481675, 'recall': 0.7639060568603214, 'f1': 0.7006802721088435, 'number': 809}      | {'precision': 0.325, 'recall': 0.2184873949579832, 'f1': 0.26130653266331655, 'number': 119}                  | {'precision': 0.6792452830188679, 'recall': 0.8112676056338028, 'f1': 0.7394094993581515, 'number': 1065}     | 0.6537            | 0.7566         | 0.7014     | 0.7820           |
-| 0.5253        | 7.0   | 70   | 0.6778          | {'precision': 0.6542056074766355, 'recall': 0.7787391841779975, 'f1': 0.711060948081264, 'number': 809}       | {'precision': 0.3047619047619048, 'recall': 0.2689075630252101, 'f1': 0.28571428571428575, 'number': 119}     | {'precision': 0.739247311827957, 'recall': 0.7746478873239436, 'f1': 0.7565337001375517, 'number': 1065}      | 0.6809            | 0.7461         | 0.7120     | 0.7896           |
-| 0.4705        | 8.0   | 80   | 0.6586          | {'precision': 0.6659751037344398, 'recall': 0.7935723114956736, 'f1': 0.7241962774957698, 'number': 809}      | {'precision': 0.30392156862745096, 'recall': 0.2605042016806723, 'f1': 0.28054298642533937, 'number': 119}    | {'precision': 0.7257093723129837, 'recall': 0.7924882629107981, 'f1': 0.7576301615798923, 'number': 1065}     | 0.6806            | 0.7612         | 0.7186     | 0.7966           |
-| 0.4214        | 9.0   | 90   | 0.6588          | {'precision': 0.6852846401718582, 'recall': 0.788627935723115, 'f1': 0.7333333333333334, 'number': 809}       | {'precision': 0.2755905511811024, 'recall': 0.29411764705882354, 'f1': 0.2845528455284553, 'number': 119}     | {'precision': 0.7396907216494846, 'recall': 0.8084507042253521, 'f1': 0.7725437415881561, 'number': 1065}     | 0.6904            | 0.7697         | 0.7279     | 0.7992           |
-| 0.3765        | 10.0  | 100  | 0.6598          | {'precision': 0.6825053995680346, 'recall': 0.7812113720642769, 'f1': 0.7285302593659942, 'number': 809}      | {'precision': 0.32142857142857145, 'recall': 0.3025210084033613, 'f1': 0.3116883116883117, 'number': 119}     | {'precision': 0.7658833768494343, 'recall': 0.8262910798122066, 'f1': 0.7949412827461607, 'number': 1065}     | 0.7078            | 0.7767         | 0.7407     | 0.8013           |
-| 0.3331        | 11.0  | 110  | 0.6659          | {'precision': 0.6778947368421052, 'recall': 0.796044499381953, 'f1': 0.7322342239909039, 'number': 809}       | {'precision': 0.3157894736842105, 'recall': 0.3025210084033613, 'f1': 0.30901287553648066, 'number': 119}     | {'precision': 0.772566371681416, 'recall': 0.819718309859155, 'f1': 0.7954441913439636, 'number': 1065}       | 0.7078            | 0.7792         | 0.7418     | 0.8033           |
-| 0.3192        | 12.0  | 120  | 0.6782          | {'precision': 0.6885069817400644, 'recall': 0.792336217552534, 'f1': 0.7367816091954023, 'number': 809}       | {'precision': 0.3170731707317073, 'recall': 0.3277310924369748, 'f1': 0.32231404958677684, 'number': 119}     | {'precision': 0.7828418230563002, 'recall': 0.8225352112676056, 'f1': 0.8021978021978022, 'number': 1065}     | 0.7161            | 0.7807         | 0.7470     | 0.8015           |
-| 0.3012        | 13.0  | 130  | 0.6835          | {'precision': 0.6929637526652452, 'recall': 0.8034610630407911, 'f1': 0.7441327990841443, 'number': 809}      | {'precision': 0.3252032520325203, 'recall': 0.33613445378151263, 'f1': 0.3305785123966942, 'number': 119}     | {'precision': 0.7847652790079717, 'recall': 0.831924882629108, 'f1': 0.8076572470373746, 'number': 1065}      | 0.7196            | 0.7908         | 0.7535     | 0.8025           |
-| 0.2867        | 14.0  | 140  | 0.6851          | {'precision': 0.7003257328990228, 'recall': 0.7972805933250927, 'f1': 0.7456647398843931, 'number': 809}      | {'precision': 0.3445378151260504, 'recall': 0.3445378151260504, 'f1': 0.3445378151260504, 'number': 119}      | {'precision': 0.7884444444444444, 'recall': 0.8328638497652582, 'f1': 0.8100456621004566, 'number': 1065}     | 0.7266            | 0.7893         | 0.7566     | 0.8029           |
-| 0.2827        | 15.0  | 150  | 0.6865          | {'precision': 0.6990185387131952, 'recall': 0.792336217552534, 'f1': 0.7427578215527232, 'number': 809}       | {'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119}     | {'precision': 0.7904085257548845, 'recall': 0.8356807511737089, 'f1': 0.8124144226380648, 'number': 1065}     | 0.7268            | 0.7888         | 0.7565     | 0.8047           |
+| 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           |
 
 
 ### Framework versions
 
-- Transformers 4.27.4
+- Transformers 4.28.1
 - Pytorch 2.0.0+cu118
 - Datasets 2.11.0
 - Tokenizers 0.13.3

logs/events.out.tfevents.1682457843.e3fb78822401.309.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d3a63f9bf275f6a6883e85ef946d63a642ecfa63ff282cc287cba1cce69648ea
-size 11386
+oid sha256:7f7f64ea05b335f95be1522a9235f88158d65c45ffdb80af9b8ef18f57ed6a41
+size 14372

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:91d161864497160eb55aee840af3c748d3d38094099342dbc3e1177b0d91e114
+oid sha256:90d1b959e1ea50997df071d34b4f7e285327004d21a9abca94cd41d2b036bca6
 size 450608389

tokenizer.json

@@ -1,7 +1,21 @@
 {
   "version": "1.0",
-  "truncation": null,
-  "padding": null,
+  "truncation": {
+    "direction": "Right",
+    "max_length": 512,
+    "strategy": "LongestFirst",
+    "stride": 0
+  },
+  "padding": {
+    "strategy": {
+      "Fixed": 512
+    },
+    "direction": "Right",
+    "pad_to_multiple_of": null,
+    "pad_id": 0,
+    "pad_type_id": 0,
+    "pad_token": "[PAD]"
+  },
   "added_tokens": [
     {
       "id": 0,

tokenizer_config.json

@@ -1,6 +1,7 @@
 {
   "additional_special_tokens": null,
   "apply_ocr": false,
+  "clean_up_tokenization_spaces": true,
   "cls_token": "[CLS]",
   "cls_token_box": [
     0,
@@ -30,7 +31,6 @@
     1000,
     1000
   ],
-  "special_tokens_map_file": null,
   "strip_accents": null,
   "tokenize_chinese_chars": true,
   "tokenizer_class": "LayoutLMv2Tokenizer",