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: layout-lm
+  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. -->
+
+# layout-lm
+
+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: 0.6696
+- Answer: {'precision': 0.7092896174863388, 'recall': 0.8022249690976514, 'f1': 0.7529002320185615, 'number': 809}
+- Header: {'precision': 0.26618705035971224, 'recall': 0.31092436974789917, 'f1': 0.2868217054263566, 'number': 119}
+- Question: {'precision': 0.7788632326820604, 'recall': 0.8234741784037559, 'f1': 0.8005476951163851, 'number': 1065}
+- Overall Precision: 0.7170
+- Overall Recall: 0.7842
+- Overall F1: 0.7491
+- Overall Accuracy: 0.8090
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7756        | 1.0   | 10   | 1.5443          | {'precision': 0.022977022977022976, 'recall': 0.02843016069221261, 'f1': 0.025414364640883976, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.18486486486486486, 'recall': 0.16056338028169015, 'f1': 0.17185929648241205, 'number': 1065} | 0.1007            | 0.0973         | 0.0990     | 0.3934           |
+| 1.4156        | 2.0   | 20   | 1.2218          | {'precision': 0.2844311377245509, 'recall': 0.3522867737948084, 'f1': 0.3147432357813363, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.4658151765589782, 'recall': 0.5821596244131455, 'f1': 0.5175292153589315, 'number': 1065}    | 0.3879            | 0.4541         | 0.4184     | 0.5974           |
+| 1.0947        | 3.0   | 30   | 0.9351          | {'precision': 0.45348837209302323, 'recall': 0.5784919653893696, 'f1': 0.5084193373166758, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.5731800766283525, 'recall': 0.7023474178403756, 'f1': 0.6312236286919831, 'number': 1065}    | 0.5155            | 0.6101         | 0.5588     | 0.7042           |
+| 0.8401        | 4.0   | 40   | 0.8011          | {'precision': 0.5671361502347417, 'recall': 0.7466007416563659, 'f1': 0.6446104589114193, 'number': 809}      | {'precision': 0.03636363636363636, 'recall': 0.01680672268907563, 'f1': 0.022988505747126436, 'number': 119} | {'precision': 0.6641285956006768, 'recall': 0.7370892018779343, 'f1': 0.6987093902981754, 'number': 1065}    | 0.6043            | 0.6979         | 0.6477     | 0.7447           |
+| 0.6784        | 5.0   | 50   | 0.7088          | {'precision': 0.6298568507157464, 'recall': 0.761433868974042, 'f1': 0.689423614997202, 'number': 809}        | {'precision': 0.13333333333333333, 'recall': 0.08403361344537816, 'f1': 0.10309278350515463, 'number': 119}  | {'precision': 0.6869009584664537, 'recall': 0.8075117370892019, 'f1': 0.7423392317652138, 'number': 1065}    | 0.6447            | 0.7456         | 0.6915     | 0.7832           |
+| 0.5803        | 6.0   | 60   | 0.6837          | {'precision': 0.632512315270936, 'recall': 0.7935723114956736, 'f1': 0.7039473684210525, 'number': 809}       | {'precision': 0.17, 'recall': 0.14285714285714285, 'f1': 0.15525114155251143, 'number': 119}                 | {'precision': 0.7255244755244755, 'recall': 0.7793427230046949, 'f1': 0.7514712539610684, 'number': 1065}    | 0.6591            | 0.7471         | 0.7004     | 0.7899           |
+| 0.5058        | 7.0   | 70   | 0.6616          | {'precision': 0.6632337796086509, 'recall': 0.796044499381953, 'f1': 0.7235955056179776, 'number': 809}       | {'precision': 0.22935779816513763, 'recall': 0.21008403361344538, 'f1': 0.2192982456140351, 'number': 119}   | {'precision': 0.7556917688266199, 'recall': 0.8103286384976526, 'f1': 0.7820570910738559, 'number': 1065}    | 0.6895            | 0.7687         | 0.7269     | 0.8049           |
+| 0.4504        | 8.0   | 80   | 0.6497          | {'precision': 0.6694045174537988, 'recall': 0.8059332509270705, 'f1': 0.7313516545148627, 'number': 809}      | {'precision': 0.24778761061946902, 'recall': 0.23529411764705882, 'f1': 0.2413793103448276, 'number': 119}   | {'precision': 0.7757255936675461, 'recall': 0.828169014084507, 'f1': 0.8010899182561309, 'number': 1065}     | 0.7023            | 0.7837         | 0.7408     | 0.8126           |
+| 0.4046        | 9.0   | 90   | 0.6455          | {'precision': 0.6864406779661016, 'recall': 0.8009888751545118, 'f1': 0.7393040501996578, 'number': 809}      | {'precision': 0.25396825396825395, 'recall': 0.2689075630252101, 'f1': 0.2612244897959184, 'number': 119}    | {'precision': 0.7812223206377326, 'recall': 0.828169014084507, 'f1': 0.8040109389243391, 'number': 1065}     | 0.7103            | 0.7837         | 0.7452     | 0.8152           |
+| 0.3936        | 10.0  | 100  | 0.6659          | {'precision': 0.6867088607594937, 'recall': 0.8046971569839307, 'f1': 0.7410358565737052, 'number': 809}      | {'precision': 0.24193548387096775, 'recall': 0.25210084033613445, 'f1': 0.2469135802469136, 'number': 119}   | {'precision': 0.7786811201445348, 'recall': 0.8093896713615023, 'f1': 0.7937384898710865, 'number': 1065}    | 0.7081            | 0.7742         | 0.7397     | 0.8078           |
+| 0.3364        | 11.0  | 110  | 0.6591          | {'precision': 0.6890308839190629, 'recall': 0.799752781211372, 'f1': 0.7402745995423341, 'number': 809}       | {'precision': 0.2824427480916031, 'recall': 0.31092436974789917, 'f1': 0.29600000000000004, 'number': 119}   | {'precision': 0.7735682819383259, 'recall': 0.8244131455399061, 'f1': 0.7981818181818181, 'number': 1065}    | 0.7084            | 0.7837         | 0.7442     | 0.8115           |
+| 0.3265        | 12.0  | 120  | 0.6682          | {'precision': 0.6912393162393162, 'recall': 0.799752781211372, 'f1': 0.7415472779369628, 'number': 809}       | {'precision': 0.26666666666666666, 'recall': 0.3025210084033613, 'f1': 0.28346456692913385, 'number': 119}   | {'precision': 0.7784697508896797, 'recall': 0.8215962441314554, 'f1': 0.7994518044769301, 'number': 1065}    | 0.7098            | 0.7817         | 0.7440     | 0.8077           |
+| 0.3079        | 13.0  | 130  | 0.6711          | {'precision': 0.7035830618892508, 'recall': 0.8009888751545118, 'f1': 0.7491329479768787, 'number': 809}      | {'precision': 0.26717557251908397, 'recall': 0.29411764705882354, 'f1': 0.28, 'number': 119}                 | {'precision': 0.7762114537444934, 'recall': 0.8272300469483568, 'f1': 0.8009090909090909, 'number': 1065}    | 0.7151            | 0.7847         | 0.7483     | 0.8090           |
+| 0.2868        | 14.0  | 140  | 0.6677          | {'precision': 0.704225352112676, 'recall': 0.8034610630407911, 'f1': 0.7505773672055426, 'number': 809}       | {'precision': 0.2835820895522388, 'recall': 0.31932773109243695, 'f1': 0.30039525691699603, 'number': 119}   | {'precision': 0.7804444444444445, 'recall': 0.8244131455399061, 'f1': 0.8018264840182647, 'number': 1065}    | 0.7177            | 0.7858         | 0.7502     | 0.8111           |
+| 0.2863        | 15.0  | 150  | 0.6696          | {'precision': 0.7092896174863388, 'recall': 0.8022249690976514, 'f1': 0.7529002320185615, 'number': 809}      | {'precision': 0.26618705035971224, 'recall': 0.31092436974789917, 'f1': 0.2868217054263566, 'number': 119}   | {'precision': 0.7788632326820604, 'recall': 0.8234741784037559, 'f1': 0.8005476951163851, 'number': 1065}    | 0.7170            | 0.7842         | 0.7491     | 0.8090           |
+
+
+### Framework versions
+
+- Transformers 4.43.2
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

logs/events.out.tfevents.1721891769.josh-desktop.30569.0

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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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+}