End of training

README.md

@@ -0,0 +1,81 @@
+---
+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: 1.1459
+- Answer: {'precision': 0.3920704845814978, 'recall': 0.5500618046971569, 'f1': 0.45781893004115226, 'number': 809}
+- Header: {'precision': 0.36363636363636365, 'recall': 0.2689075630252101, 'f1': 0.30917874396135264, 'number': 119}
+- Question: {'precision': 0.5136876006441223, 'recall': 0.5990610328638498, 'f1': 0.553099263112267, 'number': 1065}
+- Overall Precision: 0.4523
+- Overall Recall: 0.5595
+- Overall F1: 0.5002
+- Overall Accuracy: 0.6006
+
+## 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: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7425        | 1.0   | 10   | 1.4798          | {'precision': 0.05438311688311688, 'recall': 0.08281829419035847, 'f1': 0.06565409113179814, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.2244367417677643, 'recall': 0.2431924882629108, 'f1': 0.2334384858044164, 'number': 1065}  | 0.1366            | 0.1636         | 0.1489     | 0.3756           |
+| 1.419         | 2.0   | 20   | 1.3167          | {'precision': 0.21116377040547657, 'recall': 0.4956736711990111, 'f1': 0.2961595273264402, 'number': 809}   | {'precision': 0.08888888888888889, 'recall': 0.03361344537815126, 'f1': 0.048780487804878044, 'number': 119} | {'precision': 0.235467255334805, 'recall': 0.3004694835680751, 'f1': 0.264026402640264, 'number': 1065}    | 0.2195            | 0.3638         | 0.2738     | 0.4192           |
+| 1.2741        | 3.0   | 30   | 1.2387          | {'precision': 0.2594221105527638, 'recall': 0.5105067985166872, 'f1': 0.34402332361516036, 'number': 809}   | {'precision': 0.2702702702702703, 'recall': 0.16806722689075632, 'f1': 0.2072538860103627, 'number': 119}    | {'precision': 0.34717494894486045, 'recall': 0.4788732394366197, 'f1': 0.4025256511444357, 'number': 1065} | 0.3008            | 0.4732         | 0.3678     | 0.4611           |
+| 1.147         | 4.0   | 40   | 1.1190          | {'precision': 0.26329113924050634, 'recall': 0.5142150803461063, 'f1': 0.34826287149434904, 'number': 809}  | {'precision': 0.28, 'recall': 0.17647058823529413, 'f1': 0.21649484536082475, 'number': 119}                 | {'precision': 0.4030188679245283, 'recall': 0.5014084507042254, 'f1': 0.44686192468619246, 'number': 1065} | 0.3258            | 0.4872         | 0.3905     | 0.5426           |
+| 1.0331        | 5.0   | 50   | 1.1534          | {'precision': 0.2893436838390967, 'recall': 0.5067985166872683, 'f1': 0.36837376460017973, 'number': 809}   | {'precision': 0.2876712328767123, 'recall': 0.17647058823529413, 'f1': 0.21875000000000003, 'number': 119}   | {'precision': 0.4215817694369973, 'recall': 0.5906103286384976, 'f1': 0.4919827923347672, 'number': 1065}  | 0.3555            | 0.5319         | 0.4261     | 0.5476           |
+| 0.9715        | 6.0   | 60   | 1.1035          | {'precision': 0.3210227272727273, 'recall': 0.5587144622991347, 'f1': 0.4077582318448354, 'number': 809}    | {'precision': 0.3157894736842105, 'recall': 0.15126050420168066, 'f1': 0.2045454545454545, 'number': 119}    | {'precision': 0.46368243243243246, 'recall': 0.5154929577464789, 'f1': 0.4882169853268119, 'number': 1065} | 0.3847            | 0.5113         | 0.4390     | 0.5706           |
+| 0.8925        | 7.0   | 70   | 1.0616          | {'precision': 0.3607266435986159, 'recall': 0.515451174289246, 'f1': 0.42442748091603055, 'number': 809}    | {'precision': 0.29473684210526313, 'recall': 0.23529411764705882, 'f1': 0.2616822429906542, 'number': 119}   | {'precision': 0.4845360824742268, 'recall': 0.5737089201877934, 'f1': 0.52536543422184, 'number': 1065}    | 0.4204            | 0.5299         | 0.4688     | 0.5874           |
+| 0.8174        | 8.0   | 80   | 1.0694          | {'precision': 0.3473507148864592, 'recall': 0.5105067985166872, 'f1': 0.4134134134134134, 'number': 809}    | {'precision': 0.3373493975903614, 'recall': 0.23529411764705882, 'f1': 0.2772277227722772, 'number': 119}    | {'precision': 0.4794414274631497, 'recall': 0.5802816901408451, 'f1': 0.5250637213254036, 'number': 1065}  | 0.4135            | 0.5314         | 0.4651     | 0.5893           |
+| 0.7698        | 9.0   | 90   | 1.1272          | {'precision': 0.35641227380015733, 'recall': 0.5599505562422744, 'f1': 0.43557692307692303, 'number': 809}  | {'precision': 0.3493975903614458, 'recall': 0.24369747899159663, 'f1': 0.2871287128712871, 'number': 119}    | {'precision': 0.5008818342151675, 'recall': 0.5333333333333333, 'f1': 0.5165984538426557, 'number': 1065}  | 0.4220            | 0.5268         | 0.4686     | 0.5817           |
+| 0.7676        | 10.0  | 100  | 1.1380          | {'precision': 0.37153088630259623, 'recall': 0.5129789864029666, 'f1': 0.43094496365524404, 'number': 809}  | {'precision': 0.29523809523809524, 'recall': 0.2605042016806723, 'f1': 0.2767857142857143, 'number': 119}    | {'precision': 0.5185185185185185, 'recall': 0.5784037558685446, 'f1': 0.546826453617399, 'number': 1065}   | 0.4407            | 0.5329         | 0.4824     | 0.5958           |
+| 0.6932        | 11.0  | 110  | 1.1051          | {'precision': 0.387, 'recall': 0.4783683559950556, 'f1': 0.42786069651741293, 'number': 809}                | {'precision': 0.37037037037037035, 'recall': 0.25210084033613445, 'f1': 0.3, 'number': 119}                  | {'precision': 0.4865061998541211, 'recall': 0.6262910798122066, 'f1': 0.5476190476190477, 'number': 1065}  | 0.4421            | 0.5439         | 0.4877     | 0.6026           |
+| 0.6856        | 12.0  | 120  | 1.1257          | {'precision': 0.38833181403828626, 'recall': 0.5265760197775031, 'f1': 0.44700944386149, 'number': 809}     | {'precision': 0.3409090909090909, 'recall': 0.25210084033613445, 'f1': 0.2898550724637681, 'number': 119}    | {'precision': 0.48674521354933725, 'recall': 0.6206572769953052, 'f1': 0.545604622368964, 'number': 1065}  | 0.4392            | 0.5605         | 0.4925     | 0.6021           |
+| 0.6592        | 13.0  | 130  | 1.1253          | {'precision': 0.39461883408071746, 'recall': 0.5438813349814586, 'f1': 0.4573804573804573, 'number': 809}   | {'precision': 0.3614457831325301, 'recall': 0.25210084033613445, 'f1': 0.297029702970297, 'number': 119}     | {'precision': 0.5112179487179487, 'recall': 0.5990610328638498, 'f1': 0.5516645049718979, 'number': 1065}  | 0.4530            | 0.5559         | 0.4992     | 0.6066           |
+| 0.6358        | 14.0  | 140  | 1.1420          | {'precision': 0.3906810035842294, 'recall': 0.5389369592088998, 'f1': 0.452987012987013, 'number': 809}     | {'precision': 0.36904761904761907, 'recall': 0.2605042016806723, 'f1': 0.30541871921182273, 'number': 119}   | {'precision': 0.5062597809076682, 'recall': 0.6075117370892019, 'f1': 0.5522833973538199, 'number': 1065}  | 0.4496            | 0.5590         | 0.4983     | 0.6018           |
+| 0.6263        | 15.0  | 150  | 1.1459          | {'precision': 0.3920704845814978, 'recall': 0.5500618046971569, 'f1': 0.45781893004115226, 'number': 809}   | {'precision': 0.36363636363636365, 'recall': 0.2689075630252101, 'f1': 0.30917874396135264, 'number': 119}   | {'precision': 0.5136876006441223, 'recall': 0.5990610328638498, 'f1': 0.553099263112267, 'number': 1065}   | 0.4523            | 0.5595         | 0.5002     | 0.6006           |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 2.2.2+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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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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+}