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: 0.6573
+- Answer: {'precision': 0.7060773480662983, 'recall': 0.7898640296662547, 'f1': 0.7456242707117853, 'number': 809}
+- Header: {'precision': 0.3333333333333333, 'recall': 0.3697478991596639, 'f1': 0.350597609561753, 'number': 119}
+- Question: {'precision': 0.7687661777394306, 'recall': 0.8366197183098592, 'f1': 0.8012589928057554, 'number': 1065}
+- Overall Precision: 0.7168
+- Overall Recall: 0.7898
+- Overall F1: 0.7515
+- Overall Accuracy: 0.8172
+
+## 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
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                       | Header                                                                                                      | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7999        | 1.0   | 10   | 1.5802          | {'precision': 0.008905852417302799, 'recall': 0.00865265760197775, 'f1': 0.00877742946708464, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.1717325227963526, 'recall': 0.10610328638497653, 'f1': 0.13116656993615786, 'number': 1065} | 0.0831            | 0.0602         | 0.0698     | 0.3604           |
+| 1.4567        | 2.0   | 20   | 1.2493          | {'precision': 0.18839103869653767, 'recall': 0.22867737948084055, 'f1': 0.20658849804578447, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.45693950177935944, 'recall': 0.6028169014084507, 'f1': 0.5198380566801619, 'number': 1065}  | 0.3465            | 0.4150         | 0.3776     | 0.5986           |
+| 1.114         | 3.0   | 30   | 0.9406          | {'precision': 0.43853820598006643, 'recall': 0.4894932014833127, 'f1': 0.46261682242990654, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5861538461538461, 'recall': 0.7154929577464789, 'f1': 0.6443974630021141, 'number': 1065}   | 0.5237            | 0.5810         | 0.5509     | 0.7001           |
+| 0.8434        | 4.0   | 40   | 0.7906          | {'precision': 0.5922836287799792, 'recall': 0.7021013597033374, 'f1': 0.6425339366515838, 'number': 809}     | {'precision': 0.1111111111111111, 'recall': 0.04201680672268908, 'f1': 0.06097560975609755, 'number': 119}  | {'precision': 0.6526994359387591, 'recall': 0.7605633802816901, 'f1': 0.7025151777970512, 'number': 1065}   | 0.6160            | 0.6939         | 0.6527     | 0.7541           |
+| 0.6817        | 5.0   | 50   | 0.7106          | {'precision': 0.6502192982456141, 'recall': 0.7330037082818294, 'f1': 0.6891342242882045, 'number': 809}     | {'precision': 0.25301204819277107, 'recall': 0.17647058823529413, 'f1': 0.20792079207920794, 'number': 119} | {'precision': 0.683921568627451, 'recall': 0.8187793427230047, 'f1': 0.7452991452991454, 'number': 1065}    | 0.6546            | 0.7456         | 0.6972     | 0.7854           |
+| 0.5737        | 6.0   | 60   | 0.6807          | {'precision': 0.6482617586912065, 'recall': 0.7836835599505563, 'f1': 0.7095691102406267, 'number': 809}     | {'precision': 0.273972602739726, 'recall': 0.16806722689075632, 'f1': 0.20833333333333331, 'number': 119}   | {'precision': 0.717206132879046, 'recall': 0.7906103286384977, 'f1': 0.7521214828048235, 'number': 1065}    | 0.6724            | 0.7506         | 0.7093     | 0.7898           |
+| 0.5058        | 7.0   | 70   | 0.6538          | {'precision': 0.6564102564102564, 'recall': 0.7911001236093943, 'f1': 0.7174887892376681, 'number': 809}     | {'precision': 0.3048780487804878, 'recall': 0.21008403361344538, 'f1': 0.24875621890547264, 'number': 119}  | {'precision': 0.7324894514767932, 'recall': 0.8150234741784037, 'f1': 0.7715555555555556, 'number': 1065}   | 0.6838            | 0.7692         | 0.7240     | 0.7996           |
+| 0.4425        | 8.0   | 80   | 0.6574          | {'precision': 0.6625766871165644, 'recall': 0.8009888751545118, 'f1': 0.7252378287632905, 'number': 809}     | {'precision': 0.3055555555555556, 'recall': 0.2773109243697479, 'f1': 0.2907488986784141, 'number': 119}    | {'precision': 0.7365771812080537, 'recall': 0.8244131455399061, 'f1': 0.7780239255649092, 'number': 1065}   | 0.6844            | 0.7822         | 0.7300     | 0.7999           |
+| 0.3932        | 9.0   | 90   | 0.6375          | {'precision': 0.6876971608832808, 'recall': 0.8084054388133498, 'f1': 0.7431818181818182, 'number': 809}     | {'precision': 0.3645833333333333, 'recall': 0.29411764705882354, 'f1': 0.3255813953488372, 'number': 119}   | {'precision': 0.752129471890971, 'recall': 0.8291079812206573, 'f1': 0.7887449754354622, 'number': 1065}    | 0.7078            | 0.7888         | 0.7461     | 0.8087           |
+| 0.3798        | 10.0  | 100  | 0.6437          | {'precision': 0.6981541802388708, 'recall': 0.7948084054388134, 'f1': 0.7433526011560695, 'number': 809}     | {'precision': 0.325, 'recall': 0.3277310924369748, 'f1': 0.3263598326359833, 'number': 119}                 | {'precision': 0.7665505226480837, 'recall': 0.8262910798122066, 'f1': 0.7953004970628107, 'number': 1065}   | 0.7136            | 0.7837         | 0.7470     | 0.8098           |
+| 0.3225        | 11.0  | 110  | 0.6566          | {'precision': 0.6817226890756303, 'recall': 0.8022249690976514, 'f1': 0.7370812038614423, 'number': 809}     | {'precision': 0.336, 'recall': 0.35294117647058826, 'f1': 0.3442622950819672, 'number': 119}                | {'precision': 0.7593856655290102, 'recall': 0.8356807511737089, 'f1': 0.7957085382208315, 'number': 1065}   | 0.7030            | 0.7933         | 0.7454     | 0.8038           |
+| 0.3097        | 12.0  | 120  | 0.6421          | {'precision': 0.6957928802588996, 'recall': 0.7972805933250927, 'f1': 0.7430875576036866, 'number': 809}     | {'precision': 0.35, 'recall': 0.35294117647058826, 'f1': 0.35146443514644354, 'number': 119}                | {'precision': 0.7692307692307693, 'recall': 0.8356807511737089, 'f1': 0.8010801080108011, 'number': 1065}   | 0.7155            | 0.7913         | 0.7515     | 0.8177           |
+| 0.2916        | 13.0  | 130  | 0.6515          | {'precision': 0.7035010940919038, 'recall': 0.7948084054388134, 'f1': 0.7463726059199072, 'number': 809}     | {'precision': 0.33076923076923076, 'recall': 0.36134453781512604, 'f1': 0.34538152610441764, 'number': 119} | {'precision': 0.7649092480553155, 'recall': 0.8309859154929577, 'f1': 0.7965796579657966, 'number': 1065}   | 0.7138            | 0.7883         | 0.7492     | 0.8154           |
+| 0.2707        | 14.0  | 140  | 0.6557          | {'precision': 0.7016393442622951, 'recall': 0.7935723114956736, 'f1': 0.7447795823665894, 'number': 809}     | {'precision': 0.3333333333333333, 'recall': 0.36134453781512604, 'f1': 0.34677419354838707, 'number': 119}  | {'precision': 0.7688966116420504, 'recall': 0.8309859154929577, 'f1': 0.7987364620938627, 'number': 1065}   | 0.7153            | 0.7878         | 0.7498     | 0.8146           |
+| 0.2729        | 15.0  | 150  | 0.6573          | {'precision': 0.7060773480662983, 'recall': 0.7898640296662547, 'f1': 0.7456242707117853, 'number': 809}     | {'precision': 0.3333333333333333, 'recall': 0.3697478991596639, 'f1': 0.350597609561753, 'number': 119}     | {'precision': 0.7687661777394306, 'recall': 0.8366197183098592, 'f1': 0.8012589928057554, 'number': 1065}   | 0.7168            | 0.7898         | 0.7515     | 0.8172           |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.3.0+cpu
+- Datasets 2.19.0
+- Tokenizers 0.19.1

logs/events.out.tfevents.1714647793.DESKTOP-GACPI3E.7908.0

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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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+}