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: 1.1352
-- Answer: {'precision': 0.38767395626242546, 'recall': 0.4820766378244747, 'f1': 0.42975206611570255, 'number': 809}
-- Header: {'precision': 0.3181818181818182, 'recall': 0.23529411764705882, 'f1': 0.27053140096618356, 'number': 119}
-- Question: {'precision': 0.4954954954954955, 'recall': 0.6197183098591549, 'f1': 0.5506883604505632, 'number': 1065}
-- Overall Precision: 0.4444
-- Overall Recall: 0.5409
-- Overall F1: 0.4879
-- Overall Accuracy: 0.6048
+- Loss: 1.1882
+- Answer: {'precision': 0.24169381107491858, 'recall': 0.45859085290482077, 'f1': 0.31655290102389083, 'number': 809}
+- Header: {'precision': 0.08955223880597014, 'recall': 0.05042016806722689, 'f1': 0.06451612903225806, 'number': 119}
+- Question: {'precision': 0.35651074589127685, 'recall': 0.5295774647887324, 'f1': 0.4261428031734038, 'number': 1065}
+- Overall Precision: 0.2955
+- Overall Recall: 0.4722
+- Overall F1: 0.3635
+- Overall Accuracy: 0.4847
 
 ## Model description
 
@@ -49,27 +49,17 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 15
+- num_epochs: 5
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                       | Header                                                                                                      | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.7173        | 1.0   | 10   | 1.5055          | {'precision': 0.036076662908680945, 'recall': 0.03955500618046971, 'f1': 0.03773584905660377, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.2727272727272727, 'recall': 0.18873239436619718, 'f1': 0.22308546059933407, 'number': 1065} | 0.1435            | 0.1169         | 0.1288     | 0.3597           |
-| 1.4183        | 2.0   | 20   | 1.3144          | {'precision': 0.18861414606095459, 'recall': 0.4054388133498146, 'f1': 0.2574568288854003, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.24258404746209625, 'recall': 0.3455399061032864, 'f1': 0.28505034856700234, 'number': 1065} | 0.2136            | 0.3492         | 0.2650     | 0.4353           |
-| 1.257         | 3.0   | 30   | 1.1761          | {'precision': 0.2615723732549596, 'recall': 0.4400494437577256, 'f1': 0.32811059907834106, 'number': 809}    | {'precision': 0.05, 'recall': 0.01680672268907563, 'f1': 0.025157232704402517, 'number': 119}               | {'precision': 0.36586863106200124, 'recall': 0.5596244131455399, 'f1': 0.44246473645137346, 'number': 1065} | 0.3149            | 0.4787         | 0.3799     | 0.5312           |
-| 1.1319        | 4.0   | 40   | 1.0879          | {'precision': 0.3036978756884343, 'recall': 0.47713226205191595, 'f1': 0.3711538461538461, 'number': 809}    | {'precision': 0.2345679012345679, 'recall': 0.15966386554621848, 'f1': 0.18999999999999997, 'number': 119}  | {'precision': 0.42283464566929135, 'recall': 0.504225352112676, 'f1': 0.4599571734475375, 'number': 1065}   | 0.3593            | 0.4727         | 0.4082     | 0.5793           |
-| 1.0046        | 5.0   | 50   | 1.1292          | {'precision': 0.32560137457044674, 'recall': 0.4684796044499382, 'f1': 0.38418651799290426, 'number': 809}   | {'precision': 0.25301204819277107, 'recall': 0.17647058823529413, 'f1': 0.20792079207920794, 'number': 119} | {'precision': 0.4408831908831909, 'recall': 0.5812206572769953, 'f1': 0.5014175779667881, 'number': 1065}   | 0.3844            | 0.5113         | 0.4388     | 0.5817           |
-| 0.9305        | 6.0   | 60   | 1.1583          | {'precision': 0.3395311236863379, 'recall': 0.519159456118665, 'f1': 0.41055718475073316, 'number': 809}     | {'precision': 0.2835820895522388, 'recall': 0.15966386554621848, 'f1': 0.2043010752688172, 'number': 119}   | {'precision': 0.4719387755102041, 'recall': 0.5211267605633803, 'f1': 0.49531459170013387, 'number': 1065}  | 0.4008            | 0.4987         | 0.4444     | 0.5817           |
-| 0.8843        | 7.0   | 70   | 1.1142          | {'precision': 0.32987551867219916, 'recall': 0.3930778739184178, 'f1': 0.3587140439932318, 'number': 809}    | {'precision': 0.25287356321839083, 'recall': 0.18487394957983194, 'f1': 0.21359223300970878, 'number': 119} | {'precision': 0.41626794258373206, 'recall': 0.6535211267605634, 'f1': 0.5085860431128973, 'number': 1065}  | 0.3805            | 0.5198         | 0.4394     | 0.5831           |
-| 0.8326        | 8.0   | 80   | 1.0891          | {'precision': 0.33364661654135336, 'recall': 0.4388133498145859, 'f1': 0.3790710090763481, 'number': 809}    | {'precision': 0.26582278481012656, 'recall': 0.17647058823529413, 'f1': 0.2121212121212121, 'number': 119}  | {'precision': 0.42464040025015637, 'recall': 0.6375586854460094, 'f1': 0.5097597597597597, 'number': 1065}  | 0.3848            | 0.5294         | 0.4456     | 0.5943           |
-| 0.7867        | 9.0   | 90   | 1.1168          | {'precision': 0.36489151873767256, 'recall': 0.4573547589616811, 'f1': 0.40592430060340096, 'number': 809}   | {'precision': 0.27835051546391754, 'recall': 0.226890756302521, 'f1': 0.25, 'number': 119}                  | {'precision': 0.4975845410628019, 'recall': 0.5802816901408451, 'f1': 0.5357607282184654, 'number': 1065}   | 0.4314            | 0.5093         | 0.4671     | 0.5919           |
-| 0.7846        | 10.0  | 100  | 1.1754          | {'precision': 0.38025415444770283, 'recall': 0.48084054388133496, 'f1': 0.42467248908296945, 'number': 809}  | {'precision': 0.3614457831325301, 'recall': 0.25210084033613445, 'f1': 0.297029702970297, 'number': 119}    | {'precision': 0.5054945054945055, 'recall': 0.5615023474178403, 'f1': 0.5320284697508897, 'number': 1065}   | 0.4443            | 0.5103         | 0.4750     | 0.5923           |
-| 0.711         | 11.0  | 110  | 1.1427          | {'precision': 0.3814968814968815, 'recall': 0.453646477132262, 'f1': 0.41445511010728403, 'number': 809}     | {'precision': 0.32967032967032966, 'recall': 0.25210084033613445, 'f1': 0.28571428571428575, 'number': 119} | {'precision': 0.4864667154352597, 'recall': 0.6244131455399061, 'f1': 0.5468750000000001, 'number': 1065}   | 0.4388            | 0.5329         | 0.4813     | 0.6085           |
-| 0.7118        | 12.0  | 120  | 1.1172          | {'precision': 0.36363636363636365, 'recall': 0.4796044499381953, 'f1': 0.4136460554371002, 'number': 809}    | {'precision': 0.3764705882352941, 'recall': 0.2689075630252101, 'f1': 0.3137254901960785, 'number': 119}    | {'precision': 0.47493036211699163, 'recall': 0.64037558685446, 'f1': 0.5453818472610956, 'number': 1065}    | 0.4258            | 0.5529         | 0.4811     | 0.6020           |
-| 0.6891        | 13.0  | 130  | 1.1580          | {'precision': 0.3810375670840787, 'recall': 0.5265760197775031, 'f1': 0.44213803840166066, 'number': 809}    | {'precision': 0.3146067415730337, 'recall': 0.23529411764705882, 'f1': 0.2692307692307692, 'number': 119}   | {'precision': 0.5264527320034692, 'recall': 0.5699530516431925, 'f1': 0.5473399458972048, 'number': 1065}   | 0.4496            | 0.5324         | 0.4875     | 0.6035           |
-| 0.6544        | 14.0  | 140  | 1.1198          | {'precision': 0.38986556359875907, 'recall': 0.46600741656365885, 'f1': 0.4245495495495496, 'number': 809}   | {'precision': 0.3333333333333333, 'recall': 0.24369747899159663, 'f1': 0.2815533980582524, 'number': 119}   | {'precision': 0.48421807747489237, 'recall': 0.6338028169014085, 'f1': 0.5490036600244002, 'number': 1065}  | 0.4416            | 0.5424         | 0.4868     | 0.6037           |
-| 0.6515        | 15.0  | 150  | 1.1352          | {'precision': 0.38767395626242546, 'recall': 0.4820766378244747, 'f1': 0.42975206611570255, 'number': 809}   | {'precision': 0.3181818181818182, 'recall': 0.23529411764705882, 'f1': 0.27053140096618356, 'number': 119}  | {'precision': 0.4954954954954955, 'recall': 0.6197183098591549, 'f1': 0.5506883604505632, 'number': 1065}   | 0.4444            | 0.5409         | 0.4879     | 0.6048           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                                                                       | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.735         | 1.0   | 10   | 1.5463          | {'precision': 0.034482758620689655, 'recall': 0.014833127317676144, 'f1': 0.02074330164217805, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.3490990990990991, 'recall': 0.14553990610328638, 'f1': 0.20543406229290923, 'number': 1065}  | 0.2109            | 0.0838         | 0.1199     | 0.3216           |
+| 1.4649        | 2.0   | 20   | 1.3745          | {'precision': 0.16110761485210826, 'recall': 0.3164400494437577, 'f1': 0.21351125938281904, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.2809198259788689, 'recall': 0.4244131455399061, 'f1': 0.3380703066566941, 'number': 1065}    | 0.2213            | 0.3552         | 0.2727     | 0.4138           |
+| 1.3282        | 3.0   | 30   | 1.2410          | {'precision': 0.21228710462287104, 'recall': 0.43139678615574784, 'f1': 0.2845495311863025, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.31901840490797545, 'recall': 0.48826291079812206, 'f1': 0.38589981447124305, 'number': 1065} | 0.2635            | 0.4360         | 0.3285     | 0.4488           |
+| 1.2317        | 4.0   | 40   | 1.2269          | {'precision': 0.2410941475826972, 'recall': 0.4684796044499382, 'f1': 0.3183536329273415, 'number': 809}      | {'precision': 0.07142857142857142, 'recall': 0.03361344537815126, 'f1': 0.045714285714285714, 'number': 119} | {'precision': 0.34568690095846644, 'recall': 0.507981220657277, 'f1': 0.41140684410646383, 'number': 1065}   | 0.2894            | 0.4636         | 0.3563     | 0.4598           |
+| 1.1794        | 5.0   | 50   | 1.1882          | {'precision': 0.24169381107491858, 'recall': 0.45859085290482077, 'f1': 0.31655290102389083, 'number': 809}   | {'precision': 0.08955223880597014, 'recall': 0.05042016806722689, 'f1': 0.06451612903225806, 'number': 119}  | {'precision': 0.35651074589127685, 'recall': 0.5295774647887324, 'f1': 0.4261428031734038, 'number': 1065}   | 0.2955            | 0.4722         | 0.3635     | 0.4847           |
 
 
 ### Framework versions

logs/events.out.tfevents.1710831582.ccc3f2fe76fd.4084.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e7112b1252e53084999c22e393330880a1c7e452f8142f0afd1233ab91c411fe
-size 7623
+oid sha256:335877eb3cdeeed0453d45f4a3565a3cee8b17286e823a4af21b9517abfe42a4
+size 8673

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f24d8d038d3432b0b99514ac9179cb7b86dc3fe0516db90e09f38fe63fb71aff
+oid sha256:4fcdca9b04cbbe9d94788fa01a35204cef9db0ed816e3c449a3045229d1fc149
 size 450558212

tokenizer.json

@@ -1,21 +1,7 @@
 {
   "version": "1.0",
-  "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]"
-  },
+  "truncation": null,
+  "padding": null,
   "added_tokens": [
     {
       "id": 0,