End of training

README.md

@@ -2,8 +2,6 @@
 license: mit
 tags:
 - generated_from_trainer
-datasets:
-- funsd-layoutlmv3
 model-index:
 - name: new_model
   results: []
@@ -14,16 +12,16 @@ should probably proofread and complete it, then remove this comment. -->
 
 # new_model
 
-This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
+This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5095
-- Answer: {'precision': 0.8368479467258602, 'recall': 0.9228886168910648, 'f1': 0.8777648428405123, 'number': 817}
-- Header: {'precision': 0.5333333333333333, 'recall': 0.5378151260504201, 'f1': 0.5355648535564853, 'number': 119}
-- Question: {'precision': 0.8907407407407407, 'recall': 0.89322191272052, 'f1': 0.8919796012980993, 'number': 1077}
-- Overall Precision: 0.8472
-- Overall Recall: 0.8843
-- Overall F1: 0.8653
-- Overall Accuracy: 0.7922
+- Loss: 0.0292
+- Answer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}
+- Header: {'precision': 0.07692307692307693, 'recall': 0.058823529411764705, 'f1': 0.06666666666666667, 'number': 17}
+- Question: {'precision': 0.0625, 'recall': 0.058823529411764705, 'f1': 0.06060606060606061, 'number': 17}
+- Overall Precision: 0.0556
+- Overall Recall: 0.0455
+- Overall F1: 0.0500
+- Overall Accuracy: 0.6578
 
 ## Model description
 
@@ -52,20 +50,20 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.1542        | 3.51  | 200  | 0.2369          | {'precision': 0.7753846153846153, 'recall': 0.9253365973072215, 'f1': 0.8437500000000001, 'number': 817} | {'precision': 0.4166666666666667, 'recall': 0.21008403361344538, 'f1': 0.2793296089385475, 'number': 119}  | {'precision': 0.8449744463373083, 'recall': 0.9210770659238626, 'f1': 0.8813860506441582, 'number': 1077} | 0.8026            | 0.8808         | 0.8399     | 0.7940           |
-| 0.0493        | 7.02  | 400  | 0.2901          | {'precision': 0.8056133056133056, 'recall': 0.9485924112607099, 'f1': 0.8712759977515458, 'number': 817} | {'precision': 0.45614035087719296, 'recall': 0.4369747899159664, 'f1': 0.44635193133047213, 'number': 119} | {'precision': 0.888673765730881, 'recall': 0.8523676880222841, 'f1': 0.8701421800947867, 'number': 1077}  | 0.8274            | 0.8669         | 0.8467     | 0.8010           |
-| 0.0214        | 10.53 | 600  | 0.3571          | {'precision': 0.8139013452914798, 'recall': 0.8886168910648715, 'f1': 0.8496196606202459, 'number': 817} | {'precision': 0.4186046511627907, 'recall': 0.6050420168067226, 'f1': 0.4948453608247423, 'number': 119}   | {'precision': 0.8652946679139383, 'recall': 0.8588672237697307, 'f1': 0.8620689655172413, 'number': 1077} | 0.8078            | 0.8559         | 0.8312     | 0.7907           |
-| 0.0112        | 14.04 | 800  | 0.4018          | {'precision': 0.8484455958549223, 'recall': 0.8017135862913096, 'f1': 0.8244178728760226, 'number': 817} | {'precision': 0.45, 'recall': 0.6050420168067226, 'f1': 0.5161290322580645, 'number': 119}                 | {'precision': 0.8385689354275742, 'recall': 0.8922934076137419, 'f1': 0.8645973909131804, 'number': 1077} | 0.8123            | 0.8385         | 0.8252     | 0.7872           |
-| 0.0058        | 17.54 | 1000 | 0.4571          | {'precision': 0.8466666666666667, 'recall': 0.9326805385556916, 'f1': 0.8875946418171229, 'number': 817} | {'precision': 0.6621621621621622, 'recall': 0.4117647058823529, 'f1': 0.5077720207253885, 'number': 119}   | {'precision': 0.8809738503155996, 'recall': 0.9071494893221913, 'f1': 0.8938700823421775, 'number': 1077} | 0.8584            | 0.8882         | 0.8730     | 0.7944           |
-| 0.0031        | 21.05 | 1200 | 0.4573          | {'precision': 0.8208469055374593, 'recall': 0.9253365973072215, 'f1': 0.8699654775604143, 'number': 817} | {'precision': 0.5727272727272728, 'recall': 0.5294117647058824, 'f1': 0.5502183406113538, 'number': 119}   | {'precision': 0.8799266727772685, 'recall': 0.8913649025069638, 'f1': 0.8856088560885608, 'number': 1077} | 0.8384            | 0.8838         | 0.8605     | 0.7891           |
-| 0.0019        | 24.56 | 1400 | 0.4631          | {'precision': 0.8372352285395763, 'recall': 0.9192166462668299, 'f1': 0.8763127187864644, 'number': 817} | {'precision': 0.559322033898305, 'recall': 0.5546218487394958, 'f1': 0.5569620253164557, 'number': 119}    | {'precision': 0.8864059590316573, 'recall': 0.8839368616527391, 'f1': 0.8851696885169689, 'number': 1077} | 0.8468            | 0.8788         | 0.8625     | 0.7977           |
-| 0.0011        | 28.07 | 1600 | 0.5118          | {'precision': 0.838074398249453, 'recall': 0.9375764993880049, 'f1': 0.8850375505488157, 'number': 817}  | {'precision': 0.5862068965517241, 'recall': 0.5714285714285714, 'f1': 0.5787234042553192, 'number': 119}   | {'precision': 0.885972850678733, 'recall': 0.9090064995357474, 'f1': 0.8973418881759853, 'number': 1077}  | 0.8492            | 0.9006         | 0.8742     | 0.7970           |
-| 0.0006        | 31.58 | 1800 | 0.4786          | {'precision': 0.8383500557413601, 'recall': 0.9204406364749081, 'f1': 0.8774795799299884, 'number': 817} | {'precision': 0.6, 'recall': 0.5546218487394958, 'f1': 0.5764192139737991, 'number': 119}                  | {'precision': 0.8851412944393802, 'recall': 0.9015784586815228, 'f1': 0.8932842686292549, 'number': 1077} | 0.8503            | 0.8887         | 0.8691     | 0.7942           |
-| 0.0004        | 35.09 | 2000 | 0.4959          | {'precision': 0.8338945005611672, 'recall': 0.9094247246022031, 'f1': 0.870023419203747, 'number': 817}  | {'precision': 0.5172413793103449, 'recall': 0.6302521008403361, 'f1': 0.5681818181818182, 'number': 119}   | {'precision': 0.8833333333333333, 'recall': 0.8857938718662952, 'f1': 0.8845618915159944, 'number': 1077} | 0.8374            | 0.8803         | 0.8583     | 0.7919           |
-| 0.0003        | 38.6  | 2200 | 0.5023          | {'precision': 0.8292682926829268, 'recall': 0.9155446756425949, 'f1': 0.8702734147760327, 'number': 817} | {'precision': 0.5470085470085471, 'recall': 0.5378151260504201, 'f1': 0.5423728813559322, 'number': 119}   | {'precision': 0.8771610555050046, 'recall': 0.8950789229340761, 'f1': 0.8860294117647057, 'number': 1077} | 0.8385            | 0.8823         | 0.8598     | 0.7948           |
-| 0.0002        | 42.11 | 2400 | 0.5095          | {'precision': 0.8368479467258602, 'recall': 0.9228886168910648, 'f1': 0.8777648428405123, 'number': 817} | {'precision': 0.5333333333333333, 'recall': 0.5378151260504201, 'f1': 0.5355648535564853, 'number': 119}   | {'precision': 0.8907407407407407, 'recall': 0.89322191272052, 'f1': 0.8919796012980993, 'number': 1077}   | 0.8472            | 0.8843         | 0.8653     | 0.7922           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                     | Header                                                                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.1533        | 3.92  | 200  | 0.0173          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}                                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}                                                   | 0.0               | 0.0            | 0.0        | 0.5989           |
+| 0.0443        | 7.84  | 400  | 0.0137          | {'precision': 0.09090909090909091, 'recall': 0.1, 'f1': 0.09523809523809525, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}                                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}                                                   | 0.0164            | 0.0227         | 0.0190     | 0.6203           |
+| 0.0194        | 11.76 | 600  | 0.0162          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.2222222222222222, 'recall': 0.11764705882352941, 'f1': 0.15384615384615383, 'number': 17}   | {'precision': 0.07142857142857142, 'recall': 0.058823529411764705, 'f1': 0.06451612903225808, 'number': 17}  | 0.1154            | 0.0682         | 0.0857     | 0.6417           |
+| 0.0089        | 15.69 | 800  | 0.0186          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.06666666666666667, 'recall': 0.058823529411764705, 'f1': 0.0625, 'number': 17}              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}                                                   | 0.0278            | 0.0227         | 0.0250     | 0.6684           |
+| 0.0036        | 19.61 | 1000 | 0.0264          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.07692307692307693, 'recall': 0.058823529411764705, 'f1': 0.06666666666666667, 'number': 17} | {'precision': 0.0625, 'recall': 0.058823529411764705, 'f1': 0.06060606060606061, 'number': 17}               | 0.0556            | 0.0455         | 0.0500     | 0.6578           |
+| 0.0031        | 23.53 | 1200 | 0.0200          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.1111111111111111, 'recall': 0.11764705882352941, 'f1': 0.11428571428571428, 'number': 17}   | {'precision': 0.05263157894736842, 'recall': 0.058823529411764705, 'f1': 0.05555555555555555, 'number': 17}  | 0.0732            | 0.0682         | 0.0706     | 0.6791           |
+| 0.0015        | 27.45 | 1400 | 0.0222          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.05, 'recall': 0.058823529411764705, 'f1': 0.05405405405405405, 'number': 17}                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}                                                   | 0.0196            | 0.0227         | 0.0211     | 0.6631           |
+| 0.0011        | 31.37 | 1600 | 0.0249          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17} | {'precision': 0.045454545454545456, 'recall': 0.058823529411764705, 'f1': 0.05128205128205128, 'number': 17} | 0.0408            | 0.0455         | 0.0430     | 0.6845           |
+| 0.0006        | 35.29 | 1800 | 0.0243          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}                                                   | 0.0213            | 0.0227         | 0.0220     | 0.6952           |
+| 0.0004        | 39.22 | 2000 | 0.0290          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.07142857142857142, 'recall': 0.058823529411764705, 'f1': 0.06451612903225808, 'number': 17} | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17}  | 0.0488            | 0.0455         | 0.0471     | 0.6578           |
+| 0.0002        | 43.14 | 2200 | 0.0288          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.07142857142857142, 'recall': 0.058823529411764705, 'f1': 0.06451612903225808, 'number': 17} | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17}  | 0.0488            | 0.0455         | 0.0471     | 0.6578           |
+| 0.0002        | 47.06 | 2400 | 0.0292          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.07692307692307693, 'recall': 0.058823529411764705, 'f1': 0.06666666666666667, 'number': 17} | {'precision': 0.0625, 'recall': 0.058823529411764705, 'f1': 0.06060606060606061, 'number': 17}               | 0.0556            | 0.0455         | 0.0500     | 0.6578           |
 
 
 ### Framework versions

logs/events.out.tfevents.1695379633.Benjamins-MacBook-Pro.local.86295.0

@@ -1,3 +1,3 @@
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-size 12302
+oid sha256:90a72f2cd0f8ae6d2228feb65e92b15ef080e380a5942f61a449979608b26edd
+size 12656

pytorch_model.bin

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 version https://git-lfs.github.com/spec/v1
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+oid sha256:123abf32bf6639215964c60e28b3734657e1ba6f191fede587b082600a0735d7
 size 520816014

tokenizer.json

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