End of training

README.md

@@ -0,0 +1,80 @@
+---
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutlm-funsd
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# layoutlm-funsd
+
+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: 1.2144
+- Answer: {'precision': 0.46146146146146144, 'recall': 0.5698393077873919, 'f1': 0.5099557522123893, 'number': 809}
+- Header: {'precision': 0.4024390243902439, 'recall': 0.2773109243697479, 'f1': 0.3283582089552239, 'number': 119}
+- Question: {'precision': 0.5888412017167381, 'recall': 0.644131455399061, 'f1': 0.6152466367713004, 'number': 1065}
+- Overall Precision: 0.5254
+- Overall Recall: 0.5921
+- Overall F1: 0.5567
+- Overall Accuracy: 0.6483
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 4
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                     | Header                                                                                                     | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.5615        | 1.0   | 38   | 1.2309          | {'precision': 0.23910171730515192, 'recall': 0.44746600741656367, 'f1': 0.311665949203616, 'number': 809}  | {'precision': 0.2830188679245283, 'recall': 0.12605042016806722, 'f1': 0.1744186046511628, 'number': 119}  | {'precision': 0.35969209237228833, 'recall': 0.48262910798122066, 'f1': 0.4121892542101042, 'number': 1065} | 0.2974            | 0.4471         | 0.3572     | 0.4649           |
+| 1.1729        | 2.0   | 76   | 1.0880          | {'precision': 0.3109656301145663, 'recall': 0.46971569839307786, 'f1': 0.37419990152634175, 'number': 809} | {'precision': 0.423728813559322, 'recall': 0.21008403361344538, 'f1': 0.2808988764044944, 'number': 119}   | {'precision': 0.507488986784141, 'recall': 0.5408450704225352, 'f1': 0.5236363636363637, 'number': 1065}    | 0.4060            | 0.4922         | 0.4450     | 0.5557           |
+| 1.0126        | 3.0   | 114  | 1.0622          | {'precision': 0.31921110299488675, 'recall': 0.5401730531520396, 'f1': 0.40128558310376494, 'number': 809} | {'precision': 0.38372093023255816, 'recall': 0.2773109243697479, 'f1': 0.32195121951219513, 'number': 119} | {'precision': 0.4930662557781202, 'recall': 0.6009389671361502, 'f1': 0.5416842996191282, 'number': 1065}   | 0.4032            | 0.5569         | 0.4678     | 0.5662           |
+| 0.9042        | 4.0   | 152  | 1.0144          | {'precision': 0.3859060402684564, 'recall': 0.5686032138442522, 'f1': 0.45977011494252873, 'number': 809}  | {'precision': 0.312, 'recall': 0.3277310924369748, 'f1': 0.31967213114754095, 'number': 119}               | {'precision': 0.542713567839196, 'recall': 0.6084507042253521, 'f1': 0.5737051792828685, 'number': 1065}    | 0.4568            | 0.5755         | 0.5093     | 0.6204           |
+| 0.7609        | 5.0   | 190  | 1.0307          | {'precision': 0.41357466063348414, 'recall': 0.5648949320148331, 'f1': 0.47753396029258094, 'number': 809} | {'precision': 0.3373493975903614, 'recall': 0.23529411764705882, 'f1': 0.2772277227722772, 'number': 119}  | {'precision': 0.5755711775043937, 'recall': 0.6150234741784038, 'f1': 0.5946436677258284, 'number': 1065}   | 0.4901            | 0.5720         | 0.5279     | 0.6340           |
+| 0.6792        | 6.0   | 228  | 1.0643          | {'precision': 0.43541102077687444, 'recall': 0.595797280593325, 'f1': 0.5031315240083507, 'number': 809}   | {'precision': 0.4142857142857143, 'recall': 0.24369747899159663, 'f1': 0.3068783068783069, 'number': 119}  | {'precision': 0.5757575757575758, 'recall': 0.6065727699530516, 'f1': 0.5907636031092821, 'number': 1065}   | 0.5033            | 0.5805         | 0.5391     | 0.6180           |
+| 0.6081        | 7.0   | 266  | 1.0222          | {'precision': 0.4691780821917808, 'recall': 0.5080346106304079, 'f1': 0.4878338278931751, 'number': 809}   | {'precision': 0.26618705035971224, 'recall': 0.31092436974789917, 'f1': 0.2868217054263566, 'number': 119} | {'precision': 0.5478056426332288, 'recall': 0.6563380281690141, 'f1': 0.5971806920119608, 'number': 1065}   | 0.5007            | 0.5755         | 0.5355     | 0.6424           |
+| 0.5218        | 8.0   | 304  | 1.0641          | {'precision': 0.42940038684719534, 'recall': 0.5488257107540173, 'f1': 0.481823114487249, 'number': 809}   | {'precision': 0.3409090909090909, 'recall': 0.25210084033613445, 'f1': 0.2898550724637681, 'number': 119}  | {'precision': 0.5291512915129152, 'recall': 0.6732394366197183, 'f1': 0.5925619834710744, 'number': 1065}   | 0.4808            | 0.5976         | 0.5329     | 0.6167           |
+| 0.468         | 9.0   | 342  | 1.1145          | {'precision': 0.4584942084942085, 'recall': 0.5871446229913473, 'f1': 0.5149051490514905, 'number': 809}   | {'precision': 0.3924050632911392, 'recall': 0.2605042016806723, 'f1': 0.31313131313131315, 'number': 119}  | {'precision': 0.5921501706484642, 'recall': 0.6516431924882629, 'f1': 0.6204738489047832, 'number': 1065}   | 0.5247            | 0.6021         | 0.5607     | 0.6527           |
+| 0.4159        | 10.0  | 380  | 1.1606          | {'precision': 0.4683281412253375, 'recall': 0.5574783683559951, 'f1': 0.5090293453724605, 'number': 809}   | {'precision': 0.367816091954023, 'recall': 0.2689075630252101, 'f1': 0.31067961165048547, 'number': 119}   | {'precision': 0.5958369470945359, 'recall': 0.6450704225352113, 'f1': 0.6194770063119928, 'number': 1065}   | 0.5311            | 0.5871         | 0.5577     | 0.6521           |
+| 0.3764        | 11.0  | 418  | 1.2370          | {'precision': 0.4515828677839851, 'recall': 0.5995055624227441, 'f1': 0.5151354221986192, 'number': 809}   | {'precision': 0.3888888888888889, 'recall': 0.29411764705882354, 'f1': 0.3349282296650718, 'number': 119}  | {'precision': 0.6041083099906629, 'recall': 0.6075117370892019, 'f1': 0.6058052434456929, 'number': 1065}   | 0.5221            | 0.5855         | 0.5520     | 0.6248           |
+| 0.3393        | 12.0  | 456  | 1.2263          | {'precision': 0.46161515453639085, 'recall': 0.5723114956736712, 'f1': 0.5110375275938189, 'number': 809}  | {'precision': 0.35135135135135137, 'recall': 0.2184873949579832, 'f1': 0.26943005181347146, 'number': 119} | {'precision': 0.5891132572431957, 'recall': 0.6300469483568075, 'f1': 0.6088929219600726, 'number': 1065}   | 0.5235            | 0.5820         | 0.5512     | 0.6369           |
+| 0.3253        | 13.0  | 494  | 1.2059          | {'precision': 0.4658590308370044, 'recall': 0.522867737948084, 'f1': 0.49271986022131625, 'number': 809}   | {'precision': 0.3402061855670103, 'recall': 0.2773109243697479, 'f1': 0.3055555555555556, 'number': 119}   | {'precision': 0.5623028391167192, 'recall': 0.6694835680751173, 'f1': 0.6112301757393913, 'number': 1065}   | 0.5143            | 0.5866         | 0.5481     | 0.6376           |
+| 0.2996        | 14.0  | 532  | 1.2311          | {'precision': 0.46296296296296297, 'recall': 0.5871446229913473, 'f1': 0.5177111716621253, 'number': 809}  | {'precision': 0.3263157894736842, 'recall': 0.2605042016806723, 'f1': 0.2897196261682243, 'number': 119}   | {'precision': 0.5991189427312775, 'recall': 0.6384976525821596, 'f1': 0.6181818181818182, 'number': 1065}   | 0.5257            | 0.5951         | 0.5582     | 0.6350           |
+| 0.2892        | 15.0  | 570  | 1.2144          | {'precision': 0.46146146146146144, 'recall': 0.5698393077873919, 'f1': 0.5099557522123893, 'number': 809}  | {'precision': 0.4024390243902439, 'recall': 0.2773109243697479, 'f1': 0.3283582089552239, 'number': 119}   | {'precision': 0.5888412017167381, 'recall': 0.644131455399061, 'f1': 0.6152466367713004, 'number': 1065}    | 0.5254            | 0.5921         | 0.5567     | 0.6483           |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1
+- Datasets 2.18.0
+- Tokenizers 0.15.2

logs/events.out.tfevents.1710936414.FM-PC-LT-280.6887.0

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preprocessor_config.json

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special_tokens_map.json

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tokenizer_config.json

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+}