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: 0.7085
-- Answer: {'precision': 0.721081081081081, 'recall': 0.8244746600741656, 'f1': 0.7693194925028833, 'number': 809}
-- Header: {'precision': 0.3252032520325203, 'recall': 0.33613445378151263, 'f1': 0.3305785123966942, 'number': 119}
-- Question: {'precision': 0.7871772039180766, 'recall': 0.8300469483568075, 'f1': 0.8080438756855575, 'number': 1065}
-- Overall Precision: 0.7328
-- Overall Recall: 0.7983
-- Overall F1: 0.7642
-- Overall Accuracy: 0.8112
+- 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 |
-|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.792         | 1.0   | 10   | 1.5932          | {'precision': 0.03648648648648649, 'recall': 0.03337453646477132, 'f1': 0.034861200774693346, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.3541114058355438, 'recall': 0.2507042253521127, 'f1': 0.29356789444749865, 'number': 1065} | 0.1968            | 0.1475         | 0.1686     | 0.3760           |
-| 1.4339        | 2.0   | 20   | 1.2410          | {'precision': 0.2177121771217712, 'recall': 0.21878862793572312, 'f1': 0.218249075215783, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.43688639551192143, 'recall': 0.5849765258215962, 'f1': 0.5002007226013649, 'number': 1065} | 0.3573            | 0.4014         | 0.3781     | 0.5877           |
-| 1.0937        | 3.0   | 30   | 0.9505          | {'precision': 0.45005149330587024, 'recall': 0.5401730531520396, 'f1': 0.4910112359550562, 'number': 809}    | {'precision': 0.045454545454545456, 'recall': 0.008403361344537815, 'f1': 0.014184397163120567, 'number': 119} | {'precision': 0.6046141607000796, 'recall': 0.7136150234741784, 'f1': 0.6546080964685616, 'number': 1065}  | 0.5324            | 0.6011         | 0.5647     | 0.7057           |
-| 0.835         | 4.0   | 40   | 0.7870          | {'precision': 0.6255274261603375, 'recall': 0.7330037082818294, 'f1': 0.6750142287990893, 'number': 809}     | {'precision': 0.19298245614035087, 'recall': 0.09243697478991597, 'f1': 0.125, 'number': 119}                  | {'precision': 0.6779220779220779, 'recall': 0.7352112676056338, 'f1': 0.7054054054054054, 'number': 1065}  | 0.6421            | 0.6959         | 0.6680     | 0.7601           |
-| 0.6644        | 5.0   | 50   | 0.7063          | {'precision': 0.6771739130434783, 'recall': 0.7700865265760197, 'f1': 0.7206477732793521, 'number': 809}     | {'precision': 0.2857142857142857, 'recall': 0.2184873949579832, 'f1': 0.24761904761904763, 'number': 119}      | {'precision': 0.6783161239078633, 'recall': 0.8018779342723005, 'f1': 0.7349397590361446, 'number': 1065}  | 0.6621            | 0.7541         | 0.7051     | 0.7872           |
-| 0.5612        | 6.0   | 60   | 0.6880          | {'precision': 0.6639593908629442, 'recall': 0.8084054388133498, 'f1': 0.7290969899665551, 'number': 809}     | {'precision': 0.26262626262626265, 'recall': 0.2184873949579832, 'f1': 0.23853211009174313, 'number': 119}     | {'precision': 0.7401229148375769, 'recall': 0.7915492957746478, 'f1': 0.76497277676951, 'number': 1065}    | 0.6851            | 0.7642         | 0.7225     | 0.7937           |
-| 0.4819        | 7.0   | 70   | 0.6610          | {'precision': 0.6937697993664202, 'recall': 0.8121137206427689, 'f1': 0.7482915717539863, 'number': 809}     | {'precision': 0.30097087378640774, 'recall': 0.2605042016806723, 'f1': 0.27927927927927926, 'number': 119}     | {'precision': 0.7568766637089619, 'recall': 0.8009389671361502, 'f1': 0.7782846715328468, 'number': 1065}  | 0.7079            | 0.7732         | 0.7391     | 0.8034           |
-| 0.4299        | 8.0   | 80   | 0.6725          | {'precision': 0.6850152905198776, 'recall': 0.830655129789864, 'f1': 0.7508379888268155, 'number': 809}      | {'precision': 0.2803738317757009, 'recall': 0.25210084033613445, 'f1': 0.2654867256637167, 'number': 119}      | {'precision': 0.7534364261168385, 'recall': 0.8234741784037559, 'f1': 0.7868999551368328, 'number': 1065}  | 0.7012            | 0.7923         | 0.7439     | 0.7950           |
-| 0.3801        | 9.0   | 90   | 0.6654          | {'precision': 0.7142857142857143, 'recall': 0.8158220024721878, 'f1': 0.7616849394114252, 'number': 809}     | {'precision': 0.3047619047619048, 'recall': 0.2689075630252101, 'f1': 0.28571428571428575, 'number': 119}      | {'precision': 0.7697715289982425, 'recall': 0.8225352112676056, 'f1': 0.7952791647753064, 'number': 1065}  | 0.7236            | 0.7868         | 0.7538     | 0.8092           |
-| 0.3757        | 10.0  | 100  | 0.6709          | {'precision': 0.7082452431289641, 'recall': 0.8281829419035847, 'f1': 0.7635327635327636, 'number': 809}     | {'precision': 0.34, 'recall': 0.2857142857142857, 'f1': 0.31050228310502287, 'number': 119}                    | {'precision': 0.7769028871391076, 'recall': 0.8338028169014085, 'f1': 0.8043478260869565, 'number': 1065}  | 0.7273            | 0.7988         | 0.7614     | 0.8145           |
-| 0.3165        | 11.0  | 110  | 0.6781          | {'precision': 0.723726977248104, 'recall': 0.8257107540173053, 'f1': 0.7713625866050808, 'number': 809}      | {'precision': 0.3046875, 'recall': 0.3277310924369748, 'f1': 0.31578947368421056, 'number': 119}               | {'precision': 0.7736842105263158, 'recall': 0.828169014084507, 'f1': 0.7999999999999999, 'number': 1065}   | 0.7252            | 0.7973         | 0.7596     | 0.8077           |
-| 0.2993        | 12.0  | 120  | 0.6894          | {'precision': 0.71875, 'recall': 0.8244746600741656, 'f1': 0.7679907887161773, 'number': 809}                | {'precision': 0.3247863247863248, 'recall': 0.31932773109243695, 'f1': 0.3220338983050848, 'number': 119}      | {'precision': 0.7823008849557522, 'recall': 0.8300469483568075, 'f1': 0.8054669703872438, 'number': 1065}  | 0.7306            | 0.7973         | 0.7625     | 0.8117           |
-| 0.2822        | 13.0  | 130  | 0.7039          | {'precision': 0.7195652173913043, 'recall': 0.8182941903584673, 'f1': 0.7657605552342395, 'number': 809}     | {'precision': 0.3125, 'recall': 0.33613445378151263, 'f1': 0.3238866396761134, 'number': 119}                  | {'precision': 0.7823008849557522, 'recall': 0.8300469483568075, 'f1': 0.8054669703872438, 'number': 1065}  | 0.7282            | 0.7958         | 0.7605     | 0.8095           |
-| 0.2595        | 14.0  | 140  | 0.7045          | {'precision': 0.72, 'recall': 0.823238566131026, 'f1': 0.7681660899653979, 'number': 809}                    | {'precision': 0.3418803418803419, 'recall': 0.33613445378151263, 'f1': 0.3389830508474576, 'number': 119}      | {'precision': 0.7912578055307761, 'recall': 0.8328638497652582, 'f1': 0.8115279048490394, 'number': 1065}  | 0.7365            | 0.7993         | 0.7666     | 0.8118           |
-| 0.2617        | 15.0  | 150  | 0.7085          | {'precision': 0.721081081081081, 'recall': 0.8244746600741656, 'f1': 0.7693194925028833, 'number': 809}      | {'precision': 0.3252032520325203, 'recall': 0.33613445378151263, 'f1': 0.3305785123966942, 'number': 119}      | {'precision': 0.7871772039180766, 'recall': 0.8300469483568075, 'f1': 0.8080438756855575, 'number': 1065}  | 0.7328            | 0.7983         | 0.7642     | 0.8112           |
+| 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

logs/events.out.tfevents.1717253099.280ef54c6982.7237.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:154fc6617bbec756a2266267eee0ba8f679eae5e1745e266664faa23d8b35426
-size 14915
+oid sha256:5880e5cd684b297efd4d420ad7d6b84f92a16d25ead4018732aefc771328df38
+size 15984

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6ff8ae1293df3c9764c5cd00d02a8e219c1b4945176d3316062e668535dfccb9
+oid sha256:9fb81bd2041dc58f8027bf0e94cdbe26e8941a67198a612adb3540190fcdc0e9
 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,