End of training

README.md

@@ -0,0 +1,82 @@
+---
+library_name: transformers
+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: 0.7010
+- Answer: {'precision': 0.6882416396979504, 'recall': 0.788627935723115, 'f1': 0.7350230414746545, 'number': 809}
+- Header: {'precision': 0.2857142857142857, 'recall': 0.3025210084033613, 'f1': 0.2938775510204082, 'number': 119}
+- Question: {'precision': 0.7771836007130125, 'recall': 0.8187793427230047, 'f1': 0.7974394147233654, 'number': 1065}
+- Overall Precision: 0.7108
+- Overall Recall: 0.7757
+- Overall F1: 0.7418
+- Overall Accuracy: 0.8054
+
+## 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: Use 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8001        | 1.0   | 10   | 1.6097          | {'precision': 0.015299026425591099, 'recall': 0.013597033374536464, 'f1': 0.014397905759162303, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.2779291553133515, 'recall': 0.19154929577464788, 'f1': 0.226792662590328, 'number': 1065}    | 0.1480            | 0.1079         | 0.1248     | 0.3542           |
+| 1.4627        | 2.0   | 20   | 1.2809          | {'precision': 0.15977175463623394, 'recall': 0.138442521631644, 'f1': 0.14834437086092714, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.42467948717948717, 'recall': 0.49765258215962443, 'f1': 0.45827929096411585, 'number': 1065} | 0.3294            | 0.3221         | 0.3257     | 0.5862           |
+| 1.1306        | 3.0   | 30   | 1.0105          | {'precision': 0.41445783132530123, 'recall': 0.4252163164400494, 'f1': 0.4197681513117754, 'number': 809}      | {'precision': 0.11764705882352941, 'recall': 0.03361344537815126, 'f1': 0.05228758169934641, 'number': 119} | {'precision': 0.5563607085346216, 'recall': 0.6488262910798122, 'f1': 0.5990463805808409, 'number': 1065}    | 0.4934            | 0.5213         | 0.5070     | 0.6955           |
+| 0.8744        | 4.0   | 40   | 0.8187          | {'precision': 0.5656565656565656, 'recall': 0.622991347342398, 'f1': 0.5929411764705883, 'number': 809}        | {'precision': 0.2641509433962264, 'recall': 0.11764705882352941, 'f1': 0.16279069767441862, 'number': 119}  | {'precision': 0.6538789428815004, 'recall': 0.72018779342723, 'f1': 0.6854334226988382, 'number': 1065}      | 0.6070            | 0.6448         | 0.6253     | 0.7427           |
+| 0.6901        | 5.0   | 50   | 0.7311          | {'precision': 0.635091496232508, 'recall': 0.7292954264524104, 'f1': 0.6789413118527043, 'number': 809}        | {'precision': 0.25925925925925924, 'recall': 0.17647058823529413, 'f1': 0.21, 'number': 119}                | {'precision': 0.6711675933280381, 'recall': 0.7934272300469484, 'f1': 0.7271944922547332, 'number': 1065}    | 0.6417            | 0.7306         | 0.6832     | 0.7707           |
+| 0.5703        | 6.0   | 60   | 0.7127          | {'precision': 0.6548856548856549, 'recall': 0.7787391841779975, 'f1': 0.7114624505928854, 'number': 809}       | {'precision': 0.25555555555555554, 'recall': 0.19327731092436976, 'f1': 0.22009569377990432, 'number': 119} | {'precision': 0.7043701799485861, 'recall': 0.7718309859154929, 'f1': 0.7365591397849461, 'number': 1065}    | 0.6647            | 0.7401         | 0.7004     | 0.7837           |
+| 0.4964        | 7.0   | 70   | 0.6823          | {'precision': 0.6729758149316509, 'recall': 0.7911001236093943, 'f1': 0.7272727272727274, 'number': 809}       | {'precision': 0.2647058823529412, 'recall': 0.226890756302521, 'f1': 0.24434389140271492, 'number': 119}    | {'precision': 0.751099384344767, 'recall': 0.8018779342723005, 'f1': 0.7756584922797457, 'number': 1065}     | 0.6945            | 0.7632         | 0.7272     | 0.7962           |
+| 0.4347        | 8.0   | 80   | 0.6763          | {'precision': 0.6754478398314014, 'recall': 0.792336217552534, 'f1': 0.7292377701934016, 'number': 809}        | {'precision': 0.23529411764705882, 'recall': 0.23529411764705882, 'f1': 0.23529411764705882, 'number': 119} | {'precision': 0.7530434782608696, 'recall': 0.8131455399061033, 'f1': 0.781941309255079, 'number': 1065}     | 0.6921            | 0.7702         | 0.7290     | 0.8003           |
+| 0.386         | 9.0   | 90   | 0.6695          | {'precision': 0.6803013993541442, 'recall': 0.7812113720642769, 'f1': 0.7272727272727273, 'number': 809}       | {'precision': 0.29411764705882354, 'recall': 0.25210084033613445, 'f1': 0.27149321266968324, 'number': 119} | {'precision': 0.762157382847038, 'recall': 0.8093896713615023, 'f1': 0.785063752276867, 'number': 1065}      | 0.7049            | 0.7647         | 0.7336     | 0.8091           |
+| 0.3668        | 10.0  | 100  | 0.6898          | {'precision': 0.6729559748427673, 'recall': 0.7935723114956736, 'f1': 0.7283040272263188, 'number': 809}       | {'precision': 0.29357798165137616, 'recall': 0.2689075630252101, 'f1': 0.28070175438596495, 'number': 119}  | {'precision': 0.7695729537366548, 'recall': 0.812206572769953, 'f1': 0.7903152124257652, 'number': 1065}     | 0.7037            | 0.7722         | 0.7364     | 0.8064           |
+| 0.3174        | 11.0  | 110  | 0.6929          | {'precision': 0.6782700421940928, 'recall': 0.7948084054388134, 'f1': 0.7319294251565168, 'number': 809}       | {'precision': 0.2903225806451613, 'recall': 0.3025210084033613, 'f1': 0.2962962962962963, 'number': 119}    | {'precision': 0.7741364038972542, 'recall': 0.8206572769953052, 'f1': 0.796718322698268, 'number': 1065}     | 0.7056            | 0.7792         | 0.7406     | 0.8031           |
+| 0.3134        | 12.0  | 120  | 0.6977          | {'precision': 0.6860215053763441, 'recall': 0.788627935723115, 'f1': 0.7337550316273721, 'number': 809}        | {'precision': 0.29411764705882354, 'recall': 0.29411764705882354, 'f1': 0.29411764705882354, 'number': 119} | {'precision': 0.7796762589928058, 'recall': 0.8140845070422535, 'f1': 0.7965089572806615, 'number': 1065}    | 0.7126            | 0.7727         | 0.7415     | 0.8053           |
+| 0.2851        | 13.0  | 130  | 0.7022          | {'precision': 0.6894679695982627, 'recall': 0.7849196538936959, 'f1': 0.7341040462427746, 'number': 809}       | {'precision': 0.2773109243697479, 'recall': 0.2773109243697479, 'f1': 0.2773109243697479, 'number': 119}    | {'precision': 0.7772848269742679, 'recall': 0.8225352112676056, 'f1': 0.7992700729927008, 'number': 1065}    | 0.7125            | 0.7747         | 0.7423     | 0.8056           |
+| 0.2693        | 14.0  | 140  | 0.7003          | {'precision': 0.6897297297297297, 'recall': 0.788627935723115, 'f1': 0.7358708189158016, 'number': 809}        | {'precision': 0.28688524590163933, 'recall': 0.29411764705882354, 'f1': 0.2904564315352697, 'number': 119}  | {'precision': 0.7755102040816326, 'recall': 0.8206572769953052, 'f1': 0.7974452554744526, 'number': 1065}    | 0.7116            | 0.7762         | 0.7425     | 0.8048           |
+| 0.2636        | 15.0  | 150  | 0.7010          | {'precision': 0.6882416396979504, 'recall': 0.788627935723115, 'f1': 0.7350230414746545, 'number': 809}        | {'precision': 0.2857142857142857, 'recall': 0.3025210084033613, 'f1': 0.2938775510204082, 'number': 119}    | {'precision': 0.7771836007130125, 'recall': 0.8187793427230047, 'f1': 0.7974394147233654, 'number': 1065}    | 0.7108            | 0.7757         | 0.7418     | 0.8054           |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

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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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