End of training

README.md

@@ -1,4 +1,5 @@
 ---
+library_name: transformers
 license: mit
 base_model: microsoft/layoutlm-base-uncased
 tags:
@@ -17,14 +18,14 @@ 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: 1.0784
-- Answer: {'precision': 0.39729990356798456, 'recall': 0.5092707045735476, 'f1': 0.44637053087757317, 'number': 809}
-- Header: {'precision': 0.2601626016260163, 'recall': 0.2689075630252101, 'f1': 0.2644628099173554, 'number': 119}
-- Question: {'precision': 0.5115384615384615, 'recall': 0.6244131455399061, 'f1': 0.5623678646934461, 'number': 1065}
-- Overall Precision: 0.4508
-- Overall Recall: 0.5564
-- Overall F1: 0.4981
-- Overall Accuracy: 0.6275
+- Loss: 0.7010
+- Eader: {'precision': 0.20833333333333334, 'recall': 0.10416666666666667, 'f1': 0.1388888888888889, 'number': 96}
+- Nswer: {'precision': 0.3401360544217687, 'recall': 0.5514705882352942, 'f1': 0.420757363253857, 'number': 272}
+- Uestion: {'precision': 0.3463414634146341, 'recall': 0.46557377049180326, 'f1': 0.39720279720279716, 'number': 305}
+- Overall Precision: 0.3359
+- Overall Recall: 0.4487
+- Overall F1: 0.3842
+- Overall Accuracy: 0.7593
 
 ## Model description
 
@@ -47,35 +48,35 @@ The following hyperparameters were used during training:
 - train_batch_size: 16
 - eval_batch_size: 8
 - seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
 - num_epochs: 15
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                      | Header                                                                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.7434        | 1.0   | 10   | 1.5471          | {'precision': 0.05161290322580645, 'recall': 0.03955500618046971, 'f1': 0.04478656403079076, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.27050359712230215, 'recall': 0.17652582159624414, 'f1': 0.21363636363636365, 'number': 1065} | 0.1673            | 0.1104         | 0.1330     | 0.3331           |
-| 1.4356        | 2.0   | 20   | 1.3534          | {'precision': 0.21909424724602203, 'recall': 0.44252163164400493, 'f1': 0.29308227589029884, 'number': 809} | {'precision': 0.04411764705882353, 'recall': 0.025210084033613446, 'f1': 0.0320855614973262, 'number': 119} | {'precision': 0.2876712328767123, 'recall': 0.39436619718309857, 'f1': 0.33267326732673264, 'number': 1065}  | 0.2470            | 0.3919         | 0.3030     | 0.4281           |
-| 1.2677        | 3.0   | 30   | 1.2046          | {'precision': 0.24696645253390434, 'recall': 0.4276885043263288, 'f1': 0.31312217194570136, 'number': 809}  | {'precision': 0.20652173913043478, 'recall': 0.15966386554621848, 'f1': 0.1800947867298578, 'number': 119}  | {'precision': 0.3335304553518628, 'recall': 0.5295774647887324, 'f1': 0.40928882438316405, 'number': 1065}   | 0.2918            | 0.4661         | 0.3589     | 0.4856           |
-| 1.1386        | 4.0   | 40   | 1.1240          | {'precision': 0.28816326530612246, 'recall': 0.4363411619283066, 'f1': 0.34709931170108166, 'number': 809}  | {'precision': 0.1875, 'recall': 0.17647058823529413, 'f1': 0.1818181818181818, 'number': 119}               | {'precision': 0.3801391524351676, 'recall': 0.564319248826291, 'f1': 0.45427059712774, 'number': 1065}       | 0.3341            | 0.4892         | 0.3971     | 0.5567           |
-| 1.0425        | 5.0   | 50   | 1.0865          | {'precision': 0.31069609507640067, 'recall': 0.45241038318912236, 'f1': 0.3683945646703573, 'number': 809}  | {'precision': 0.25609756097560976, 'recall': 0.17647058823529413, 'f1': 0.208955223880597, 'number': 119}   | {'precision': 0.41022364217252394, 'recall': 0.6028169014084507, 'f1': 0.4882129277566539, 'number': 1065}   | 0.3642            | 0.5163         | 0.4271     | 0.5740           |
-| 1.0051        | 6.0   | 60   | 1.0745          | {'precision': 0.3435185185185185, 'recall': 0.45859085290482077, 'f1': 0.39280042350449973, 'number': 809}  | {'precision': 0.22448979591836735, 'recall': 0.18487394957983194, 'f1': 0.20276497695852533, 'number': 119} | {'precision': 0.48720066061106526, 'recall': 0.5539906103286385, 'f1': 0.5184534270650263, 'number': 1065}   | 0.4115            | 0.4932         | 0.4487     | 0.5916           |
-| 0.9533        | 7.0   | 70   | 1.0560          | {'precision': 0.329126213592233, 'recall': 0.41903584672435107, 'f1': 0.36867862969004894, 'number': 809}   | {'precision': 0.22950819672131148, 'recall': 0.23529411764705882, 'f1': 0.23236514522821577, 'number': 119} | {'precision': 0.41502463054187194, 'recall': 0.6328638497652582, 'f1': 0.5013015991074748, 'number': 1065}   | 0.375             | 0.5223         | 0.4366     | 0.5919           |
-| 0.8838        | 8.0   | 80   | 1.0296          | {'precision': 0.3531047265987025, 'recall': 0.47095179233621753, 'f1': 0.4036016949152542, 'number': 809}   | {'precision': 0.211864406779661, 'recall': 0.21008403361344538, 'f1': 0.2109704641350211, 'number': 119}    | {'precision': 0.45523941707147814, 'recall': 0.615962441314554, 'f1': 0.5235434956105347, 'number': 1065}    | 0.4026            | 0.5329         | 0.4586     | 0.6141           |
-| 0.8148        | 9.0   | 90   | 1.0582          | {'precision': 0.38949454905847375, 'recall': 0.4857849196538937, 'f1': 0.43234323432343236, 'number': 809}  | {'precision': 0.2571428571428571, 'recall': 0.226890756302521, 'f1': 0.24107142857142855, 'number': 119}    | {'precision': 0.5230125523012552, 'recall': 0.5868544600938967, 'f1': 0.5530973451327434, 'number': 1065}    | 0.4526            | 0.5243         | 0.4858     | 0.6139           |
-| 0.8139        | 10.0  | 100  | 1.0429          | {'precision': 0.37296260786193675, 'recall': 0.48084054388133496, 'f1': 0.42008639308855295, 'number': 809} | {'precision': 0.24786324786324787, 'recall': 0.24369747899159663, 'f1': 0.24576271186440676, 'number': 119} | {'precision': 0.46943078004216443, 'recall': 0.6272300469483568, 'f1': 0.5369774919614148, 'number': 1065}   | 0.4204            | 0.5449         | 0.4747     | 0.6247           |
-| 0.7228        | 11.0  | 110  | 1.0542          | {'precision': 0.38454106280193234, 'recall': 0.4919653893695921, 'f1': 0.4316702819956616, 'number': 809}   | {'precision': 0.2702702702702703, 'recall': 0.25210084033613445, 'f1': 0.2608695652173913, 'number': 119}   | {'precision': 0.5042536736272235, 'recall': 0.612206572769953, 'f1': 0.5530110262934691, 'number': 1065}     | 0.4428            | 0.5419         | 0.4874     | 0.6257           |
-| 0.7193        | 12.0  | 120  | 1.0835          | {'precision': 0.3971563981042654, 'recall': 0.5179233621755254, 'f1': 0.4495708154506438, 'number': 809}    | {'precision': 0.26126126126126126, 'recall': 0.24369747899159663, 'f1': 0.25217391304347825, 'number': 119} | {'precision': 0.5153664302600472, 'recall': 0.6140845070422535, 'f1': 0.5604113110539846, 'number': 1065}    | 0.4526            | 0.5529         | 0.4977     | 0.6268           |
-| 0.687         | 13.0  | 130  | 1.0892          | {'precision': 0.4001823154056518, 'recall': 0.5426452410383189, 'f1': 0.4606505771248688, 'number': 809}    | {'precision': 0.25742574257425743, 'recall': 0.2184873949579832, 'f1': 0.23636363636363636, 'number': 119}  | {'precision': 0.5263157894736842, 'recall': 0.5915492957746479, 'f1': 0.5570291777188329, 'number': 1065}    | 0.4572            | 0.5494         | 0.4991     | 0.6255           |
-| 0.6515        | 14.0  | 140  | 1.0795          | {'precision': 0.398635477582846, 'recall': 0.5055624227441285, 'f1': 0.4457765667574932, 'number': 809}     | {'precision': 0.25862068965517243, 'recall': 0.25210084033613445, 'f1': 0.25531914893617025, 'number': 119} | {'precision': 0.5205047318611987, 'recall': 0.6197183098591549, 'f1': 0.5657951135876553, 'number': 1065}    | 0.4560            | 0.5514         | 0.4992     | 0.6262           |
-| 0.6453        | 15.0  | 150  | 1.0784          | {'precision': 0.39729990356798456, 'recall': 0.5092707045735476, 'f1': 0.44637053087757317, 'number': 809}  | {'precision': 0.2601626016260163, 'recall': 0.2689075630252101, 'f1': 0.2644628099173554, 'number': 119}    | {'precision': 0.5115384615384615, 'recall': 0.6244131455399061, 'f1': 0.5623678646934461, 'number': 1065}    | 0.4508            | 0.5564         | 0.4981     | 0.6275           |
+| Training Loss | Epoch | Step | Validation Loss | Eader                                                                                                        | Nswer                                                                                                        | Uestion                                                                                                      | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.3693        | 1.0   | 4    | 1.2778          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.010467980295566502, 'recall': 0.0625, 'f1': 0.017932489451476793, 'number': 272}             | {'precision': 0.010113780025284451, 'recall': 0.05245901639344262, 'f1': 0.01695813460519343, 'number': 305} | 0.0102            | 0.0490         | 0.0168     | 0.4230           |
+| 1.2081        | 2.0   | 8    | 1.1974          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.047841306884480746, 'recall': 0.15073529411764705, 'f1': 0.07263064658990256, 'number': 272} | {'precision': 0.05380116959064327, 'recall': 0.15081967213114755, 'f1': 0.0793103448275862, 'number': 305}   | 0.0508            | 0.1293         | 0.0730     | 0.4805           |
+| 1.0716        | 3.0   | 12   | 1.0999          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.08643457382953182, 'recall': 0.2647058823529412, 'f1': 0.13031674208144797, 'number': 272}   | {'precision': 0.10704960835509138, 'recall': 0.26885245901639343, 'f1': 0.1531279178338002, 'number': 305}   | 0.0963            | 0.2288         | 0.1356     | 0.5225           |
+| 0.855         | 4.0   | 16   | 0.9899          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.11137440758293839, 'recall': 0.34558823529411764, 'f1': 0.16845878136200718, 'number': 272}  | {'precision': 0.13172043010752688, 'recall': 0.32131147540983607, 'f1': 0.18684461391801713, 'number': 305}  | 0.1209            | 0.2853         | 0.1698     | 0.5886           |
+| 0.753         | 5.0   | 20   | 0.9095          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.1562043795620438, 'recall': 0.39338235294117646, 'f1': 0.22361546499477536, 'number': 272}   | {'precision': 0.15538461538461537, 'recall': 0.33114754098360655, 'f1': 0.21151832460732983, 'number': 305}  | 0.1558            | 0.3091         | 0.2072     | 0.6306           |
+| 0.6212        | 6.0   | 24   | 0.8551          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.19864176570458403, 'recall': 0.43014705882352944, 'f1': 0.27177700348432055, 'number': 272}  | {'precision': 0.19141323792486584, 'recall': 0.35081967213114756, 'f1': 0.2476851851851852, 'number': 305}   | 0.1951            | 0.3328         | 0.2460     | 0.6520           |
+| 0.637         | 7.0   | 28   | 0.7987          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.2270363951473137, 'recall': 0.48161764705882354, 'f1': 0.30859835100117783, 'number': 272}   | {'precision': 0.22201492537313433, 'recall': 0.3901639344262295, 'f1': 0.28299643281807374, 'number': 305}   | 0.2244            | 0.3715         | 0.2798     | 0.6884           |
+| 0.5212        | 8.0   | 32   | 0.7621          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.260707635009311, 'recall': 0.5147058823529411, 'f1': 0.34610630407911, 'number': 272}        | {'precision': 0.23529411764705882, 'recall': 0.380327868852459, 'f1': 0.2907268170426065, 'number': 305}     | 0.2471            | 0.3804         | 0.2996     | 0.7189           |
+| 0.4969        | 9.0   | 36   | 0.7494          | {'precision': 0.06666666666666667, 'recall': 0.010416666666666666, 'f1': 0.018018018018018014, 'number': 96} | {'precision': 0.283203125, 'recall': 0.5330882352941176, 'f1': 0.36989795918367346, 'number': 272}           | {'precision': 0.2857142857142857, 'recall': 0.4131147540983607, 'f1': 0.3378016085790885, 'number': 305}     | 0.2810            | 0.4042         | 0.3315     | 0.7331           |
+| 0.4764        | 10.0  | 40   | 0.7321          | {'precision': 0.21428571428571427, 'recall': 0.0625, 'f1': 0.09677419354838708, 'number': 96}                | {'precision': 0.302713987473904, 'recall': 0.5330882352941176, 'f1': 0.38615179760319573, 'number': 272}     | {'precision': 0.3036529680365297, 'recall': 0.4360655737704918, 'f1': 0.3580080753701211, 'number': 305}     | 0.3005            | 0.4220         | 0.3511     | 0.7443           |
+| 0.3805        | 11.0  | 44   | 0.7123          | {'precision': 0.22857142857142856, 'recall': 0.08333333333333333, 'f1': 0.12213740458015265, 'number': 96}   | {'precision': 0.3008474576271186, 'recall': 0.5220588235294118, 'f1': 0.3817204301075269, 'number': 272}     | {'precision': 0.3120728929384966, 'recall': 0.4491803278688525, 'f1': 0.3682795698924731, 'number': 305}     | 0.3034            | 0.4264         | 0.3545     | 0.7504           |
+| 0.3651        | 12.0  | 48   | 0.7054          | {'precision': 0.2, 'recall': 0.08333333333333333, 'f1': 0.11764705882352941, 'number': 96}                   | {'precision': 0.31947483588621445, 'recall': 0.5367647058823529, 'f1': 0.40054869684499317, 'number': 272}   | {'precision': 0.3194444444444444, 'recall': 0.4524590163934426, 'f1': 0.37449118046132973, 'number': 305}    | 0.3143            | 0.4339         | 0.3645     | 0.7532           |
+| 0.3562        | 13.0  | 52   | 0.7085          | {'precision': 0.22727272727272727, 'recall': 0.10416666666666667, 'f1': 0.14285714285714288, 'number': 96}   | {'precision': 0.3400900900900901, 'recall': 0.5551470588235294, 'f1': 0.42178770949720673, 'number': 272}    | {'precision': 0.33816425120772947, 'recall': 0.45901639344262296, 'f1': 0.3894297635605007, 'number': 305}   | 0.3337            | 0.4473         | 0.3822     | 0.7555           |
+| 0.3191        | 14.0  | 56   | 0.7046          | {'precision': 0.1836734693877551, 'recall': 0.09375, 'f1': 0.12413793103448278, 'number': 96}                | {'precision': 0.3393665158371041, 'recall': 0.5514705882352942, 'f1': 0.42016806722689076, 'number': 272}    | {'precision': 0.34057971014492755, 'recall': 0.46229508196721314, 'f1': 0.39221140472879, 'number': 305}     | 0.3315            | 0.4458         | 0.3802     | 0.7579           |
+| 0.3347        | 15.0  | 60   | 0.7010          | {'precision': 0.20833333333333334, 'recall': 0.10416666666666667, 'f1': 0.1388888888888889, 'number': 96}    | {'precision': 0.3401360544217687, 'recall': 0.5514705882352942, 'f1': 0.420757363253857, 'number': 272}      | {'precision': 0.3463414634146341, 'recall': 0.46557377049180326, 'f1': 0.39720279720279716, 'number': 305}   | 0.3359            | 0.4487         | 0.3842     | 0.7593           |
 
 
 ### Framework versions
 
-- Transformers 4.38.2
-- Pytorch 2.2.1+cu121
-- Datasets 2.18.0
-- Tokenizers 0.15.2
+- Transformers 4.48.3
+- Pytorch 2.5.1+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.0

config.json

@@ -9,23 +9,17 @@
   "hidden_size": 768,
   "id2label": {
     "0": "O",
-    "1": "B-HEADER",
-    "2": "I-HEADER",
-    "3": "B-QUESTION",
-    "4": "I-QUESTION",
-    "5": "B-ANSWER",
-    "6": "I-ANSWER"
+    "1": "Header",
+    "2": "Question",
+    "3": "Answer"
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
-    "B-ANSWER": 5,
-    "B-HEADER": 1,
-    "B-QUESTION": 3,
-    "I-ANSWER": 6,
-    "I-HEADER": 2,
-    "I-QUESTION": 4,
-    "O": 0
+    "Answer": 3,
+    "Header": 1,
+    "O": 0,
+    "Question": 2
   },
   "layer_norm_eps": 1e-12,
   "max_2d_position_embeddings": 1024,
@@ -37,7 +31,7 @@
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
   "torch_dtype": "float32",
-  "transformers_version": "4.38.2",
+  "transformers_version": "4.48.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 30522

logs/events.out.tfevents.1740560490.f94f8b22d15e.422.0

@@ -0,0 +1,3 @@
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+size 16038

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1ee163a2d0c3b03473702e54816ce63715412bc5d8b1b45949f88d973c05cf57
-size 450558212
+oid sha256:19dbbf8295e45af239466e469b1835fd45914dcd4fdb7941c40c3143350b8585
+size 450548984

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,

tokenizer_config.json

@@ -53,10 +53,13 @@
   ],
   "do_basic_tokenize": true,
   "do_lower_case": true,
+  "extra_special_tokens": {},
   "mask_token": "[MASK]",
+  "max_length": 512,
   "model_max_length": 512,
   "never_split": null,
   "only_label_first_subword": true,
+  "pad_to_multiple_of": null,
   "pad_token": "[PAD]",
   "pad_token_box": [
     0,
@@ -65,6 +68,8 @@
     0
   ],
   "pad_token_label": -100,
+  "pad_token_type_id": 0,
+  "padding_side": "right",
   "processor_class": "LayoutLMv2Processor",
   "sep_token": "[SEP]",
   "sep_token_box": [
@@ -73,8 +78,11 @@
     1000,
     1000
   ],
+  "stride": 0,
   "strip_accents": null,
   "tokenize_chinese_chars": true,
   "tokenizer_class": "LayoutLMv2Tokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
   "unk_token": "[UNK]"
 }

training_args.bin

@@ -1,3 +1,3 @@
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-size 4920
+oid sha256:3c00c0f5902e932f68ed59f4344946ae084f5f48fc7ee5691be99a6515b4cbc9
+size 5304