End of training

README.md

@@ -0,0 +1,82 @@
+---
+library_name: transformers
+license: cc-by-nc-sa-4.0
+base_model: microsoft/layoutlmv3-base
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutlmv3-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. -->
+
+# layoutlmv3-funsd
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the funsd dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.5869
+- Answer: {'precision': 0.06117908787541713, 'recall': 0.13597033374536466, 'f1': 0.08438818565400844, 'number': 809}
+- Header: {'precision': 0.015789473684210527, 'recall': 0.025210084033613446, 'f1': 0.01941747572815534, 'number': 119}
+- Question: {'precision': 0.1918819188191882, 'recall': 0.39061032863849765, 'f1': 0.257346118156511, 'number': 1065}
+- Overall Precision: 0.1273
+- Overall Recall: 0.2654
+- Overall F1: 0.1721
+- Overall Accuracy: 0.4198
+
+## 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 OptimizerNames.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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.9534        | 1.0   | 10   | 1.7563          | {'precision': 0.021798365122615803, 'recall': 0.009888751545117428, 'f1': 0.013605442176870746, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.029567053854276663, 'recall': 0.05258215962441314, 'f1': 0.037850625211220006, 'number': 1065} | 0.0283            | 0.0321         | 0.0301     | 0.2212           |
+| 1.7529        | 2.0   | 20   | 1.6621          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}                                                    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.28431372549019607, 'recall': 0.027230046948356807, 'f1': 0.049700085689802914, 'number': 1065} | 0.0769            | 0.0146         | 0.0245     | 0.3060           |
+| 1.6557        | 3.0   | 30   | 1.6846          | {'precision': 0.025611175785797437, 'recall': 0.054388133498145856, 'f1': 0.034823901859912944, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.12563044475011462, 'recall': 0.25727699530516435, 'f1': 0.16882316697473815, 'number': 1065}   | 0.0816            | 0.1596         | 0.1079     | 0.3209           |
+| 1.5482        | 4.0   | 40   | 1.6706          | {'precision': 0.03781297904956566, 'recall': 0.09147095179233622, 'f1': 0.05350686912509039, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.1303972366148532, 'recall': 0.28356807511737087, 'f1': 0.17864537119195506, 'number': 1065}    | 0.0880            | 0.1887         | 0.1200     | 0.3287           |
+| 1.4535        | 5.0   | 50   | 1.6188          | {'precision': 0.035333707234997194, 'recall': 0.07787391841779975, 'f1': 0.04861111111111111, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.16204690831556504, 'recall': 0.28544600938967135, 'f1': 0.20673240394423667, 'number': 1065}   | 0.0988            | 0.1841         | 0.1286     | 0.3580           |
+| 1.3517        | 6.0   | 60   | 1.5478          | {'precision': 0.04584221748400853, 'recall': 0.10630407911001236, 'f1': 0.06405959031657356, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.22127329192546583, 'recall': 0.2676056338028169, 'f1': 0.24224394390140241, 'number': 1065}    | 0.1147            | 0.1862         | 0.1420     | 0.4143           |
+| 1.2494        | 7.0   | 70   | 1.5328          | {'precision': 0.049443757725587144, 'recall': 0.09888751545117429, 'f1': 0.06592501030078285, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.16467780429594273, 'recall': 0.323943661971831, 'f1': 0.21835443037974686, 'number': 1065}     | 0.1114            | 0.2132         | 0.1463     | 0.4101           |
+| 1.1759        | 8.0   | 80   | 1.5335          | {'precision': 0.051237766263673, 'recall': 0.1100123609394314, 'f1': 0.06991358994501179, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.1746031746031746, 'recall': 0.3408450704225352, 'f1': 0.2309160305343511, 'number': 1065}      | 0.1157            | 0.2268         | 0.1532     | 0.4102           |
+| 1.1089        | 9.0   | 90   | 1.5206          | {'precision': 0.055843408175014396, 'recall': 0.11990111248454882, 'f1': 0.07619795758051845, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.18374558303886926, 'recall': 0.34178403755868547, 'f1': 0.23900196979645438, 'number': 1065}   | 0.1181            | 0.2313         | 0.1563     | 0.4231           |
+| 1.0817        | 10.0  | 100  | 1.5927          | {'precision': 0.05695830886670581, 'recall': 0.11990111248454882, 'f1': 0.07722929936305732, 'number': 809}    | {'precision': 0.006993006993006993, 'recall': 0.008403361344537815, 'f1': 0.007633587786259542, 'number': 119} | {'precision': 0.19786396852164137, 'recall': 0.3305164319248826, 'f1': 0.24753867791842474, 'number': 1065}    | 0.1241            | 0.2258         | 0.1602     | 0.4152           |
+| 1.025         | 11.0  | 110  | 1.5822          | {'precision': 0.058394160583941604, 'recall': 0.12855377008652658, 'f1': 0.08030888030888031, 'number': 809}   | {'precision': 0.005952380952380952, 'recall': 0.008403361344537815, 'f1': 0.006968641114982578, 'number': 119} | {'precision': 0.20356943669827104, 'recall': 0.3427230046948357, 'f1': 0.2554233729881036, 'number': 1065}     | 0.1256            | 0.2358         | 0.1639     | 0.4192           |
+| 1.0025        | 12.0  | 120  | 1.5577          | {'precision': 0.056910569105691054, 'recall': 0.1211372064276885, 'f1': 0.07743974713551956, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.19216589861751152, 'recall': 0.39154929577464787, 'f1': 0.2578052550231839, 'number': 1065}    | 0.1269            | 0.2584         | 0.1702     | 0.4225           |
+| 0.9791        | 13.0  | 130  | 1.5920          | {'precision': 0.0602655771195097, 'recall': 0.14585908529048208, 'f1': 0.08529092880375859, 'number': 809}     | {'precision': 0.015306122448979591, 'recall': 0.025210084033613446, 'f1': 0.01904761904761905, 'number': 119}  | {'precision': 0.19343945972021226, 'recall': 0.37652582159624415, 'f1': 0.2555768005098789, 'number': 1065}    | 0.1235            | 0.2619         | 0.1678     | 0.4155           |
+| 0.9566        | 14.0  | 140  | 1.5777          | {'precision': 0.06111111111111111, 'recall': 0.13597033374536466, 'f1': 0.0843234955921809, 'number': 809}     | {'precision': 0.016483516483516484, 'recall': 0.025210084033613446, 'f1': 0.019933554817275746, 'number': 119} | {'precision': 0.19855072463768117, 'recall': 0.38591549295774646, 'f1': 0.26220095693779905, 'number': 1065}   | 0.1293            | 0.2629         | 0.1734     | 0.4223           |
+| 0.9369        | 15.0  | 150  | 1.5869          | {'precision': 0.06117908787541713, 'recall': 0.13597033374536466, 'f1': 0.08438818565400844, 'number': 809}    | {'precision': 0.015789473684210527, 'recall': 0.025210084033613446, 'f1': 0.01941747572815534, 'number': 119}  | {'precision': 0.1918819188191882, 'recall': 0.39061032863849765, 'f1': 0.257346118156511, 'number': 1065}      | 0.1273            | 0.2654         | 0.1721     | 0.4198           |
+
+
+### Framework versions
+
+- Transformers 4.48.2
+- Pytorch 2.5.1+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0

logs/events.out.tfevents.1739416639.2a032f1c9f35.986.1

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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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+}