End of training

README.md

@@ -0,0 +1,77 @@
+---
+tags:
+- generated_from_trainer
+model-index:
+- name: layoutlmv2-sroie-test
+  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. -->
+
+# layoutlmv2-sroie-test
+
+This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0312
+- Address: {'precision': 0.9915751850906306, 'recall': 0.9941131302789864, 'f1': 0.992842535787321, 'number': 3907}
+- Company: {'precision': 0.966491458607096, 'recall': 0.9865861837692823, 'f1': 0.9764354463989379, 'number': 1491}
+- Date: {'precision': 1.0, 'recall': 0.985981308411215, 'f1': 0.9929411764705882, 'number': 428}
+- Total: {'precision': 0.8783068783068783, 'recall': 0.894878706199461, 'f1': 0.8865153538050735, 'number': 371}
+- Overall Precision: 0.9792
+- Overall Recall: 0.9858
+- Overall F1: 0.9825
+- Overall Accuracy: 0.9947
+
+## 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 | Address                                                                                                   | Company                                                                                                   | Date                                                                                                     | Total                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.3558        | 1.0   | 40   | 0.0698          | {'precision': 0.9855256475368207, 'recall': 0.9933452777066804, 'f1': 0.9894200127469727, 'number': 3907} | {'precision': 0.8677685950413223, 'recall': 0.9859154929577465, 'f1': 0.9230769230769231, 'number': 1491} | {'precision': 0.8384458077709611, 'recall': 0.9579439252336449, 'f1': 0.8942202835332607, 'number': 428} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 371}                                              | 0.9412            | 0.9296         | 0.9354     | 0.9808           |
+| 0.0489        | 2.0   | 80   | 0.0374          | {'precision': 0.9917547023962896, 'recall': 0.9851548502687484, 'f1': 0.9884437596302004, 'number': 3907} | {'precision': 0.92625, 'recall': 0.993963782696177, 'f1': 0.9589129731478485, 'number': 1491}             | {'precision': 0.9699074074074074, 'recall': 0.9789719626168224, 'f1': 0.9744186046511628, 'number': 428} | {'precision': 0.7655367231638418, 'recall': 0.7304582210242587, 'f1': 0.7475862068965516, 'number': 371} | 0.9607            | 0.9716         | 0.9661     | 0.9899           |
+| 0.0282        | 3.0   | 120  | 0.0277          | {'precision': 0.9913198876691346, 'recall': 0.993857179421551, 'f1': 0.9925869120654397, 'number': 3907}  | {'precision': 0.9633986928104575, 'recall': 0.98859825620389, 'f1': 0.9758358159549818, 'number': 1491}   | {'precision': 0.9929078014184397, 'recall': 0.9813084112149533, 'f1': 0.9870740305522915, 'number': 428} | {'precision': 0.8376068376068376, 'recall': 0.7924528301886793, 'f1': 0.8144044321329641, 'number': 371} | 0.9759            | 0.9797         | 0.9778     | 0.9933           |
+| 0.0194        | 4.0   | 160  | 0.0259          | {'precision': 0.990063694267516, 'recall': 0.9946250319938572, 'f1': 0.9923391215526048, 'number': 3907}  | {'precision': 0.9754152823920266, 'recall': 0.9845741113346748, 'f1': 0.9799732977303072, 'number': 1491} | {'precision': 0.9929245283018868, 'recall': 0.9836448598130841, 'f1': 0.9882629107981221, 'number': 428} | {'precision': 0.8320209973753281, 'recall': 0.8544474393530997, 'f1': 0.8430851063829787, 'number': 371} | 0.9771            | 0.9831         | 0.9801     | 0.9939           |
+| 0.0148        | 5.0   | 200  | 0.0259          | {'precision': 0.990316004077472, 'recall': 0.9946250319938572, 'f1': 0.9924658408887755, 'number': 3907}  | {'precision': 0.9597141000649773, 'recall': 0.9906103286384976, 'f1': 0.9749174917491749, 'number': 1491} | {'precision': 0.9952941176470588, 'recall': 0.9883177570093458, 'f1': 0.9917936694021102, 'number': 428} | {'precision': 0.8621621621621621, 'recall': 0.8598382749326146, 'f1': 0.8609986504723346, 'number': 371} | 0.9756            | 0.9852         | 0.9803     | 0.9940           |
+| 0.0113        | 6.0   | 240  | 0.0255          | {'precision': 0.9910714285714286, 'recall': 0.9943690811364219, 'f1': 0.9927175162897662, 'number': 3907} | {'precision': 0.9659239842726082, 'recall': 0.98859825620389, 'f1': 0.9771295989393438, 'number': 1491}   | {'precision': 0.9976415094339622, 'recall': 0.9883177570093458, 'f1': 0.9929577464788731, 'number': 428} | {'precision': 0.9008746355685131, 'recall': 0.8328840970350404, 'f1': 0.8655462184873949, 'number': 371} | 0.9804            | 0.9829         | 0.9816     | 0.9944           |
+| 0.0094        | 7.0   | 280  | 0.0267          | {'precision': 0.9908233494774408, 'recall': 0.9948809828512926, 'f1': 0.9928480204342274, 'number': 3907} | {'precision': 0.9627450980392157, 'recall': 0.9879275653923542, 'f1': 0.9751737835153922, 'number': 1491} | {'precision': 0.9952830188679245, 'recall': 0.985981308411215, 'f1': 0.9906103286384976, 'number': 428}  | {'precision': 0.8787878787878788, 'recall': 0.8598382749326146, 'f1': 0.8692098092643051, 'number': 371} | 0.9777            | 0.9845         | 0.9811     | 0.9942           |
+| 0.0082        | 8.0   | 320  | 0.0274          | {'precision': 0.9915751850906306, 'recall': 0.9941131302789864, 'f1': 0.992842535787321, 'number': 3907}  | {'precision': 0.9671268902038133, 'recall': 0.9865861837692823, 'f1': 0.9767596281540504, 'number': 1491} | {'precision': 0.9929577464788732, 'recall': 0.9883177570093458, 'f1': 0.990632318501171, 'number': 428}  | {'precision': 0.8898071625344353, 'recall': 0.8706199460916442, 'f1': 0.880108991825613, 'number': 371}  | 0.9798            | 0.9845         | 0.9821     | 0.9946           |
+| 0.0069        | 9.0   | 360  | 0.0273          | {'precision': 0.9915751850906306, 'recall': 0.9941131302789864, 'f1': 0.992842535787321, 'number': 3907}  | {'precision': 0.972203838517538, 'recall': 0.9852448021462106, 'f1': 0.9786808794137242, 'number': 1491}  | {'precision': 1.0, 'recall': 0.9813084112149533, 'f1': 0.9905660377358491, 'number': 428}                | {'precision': 0.88, 'recall': 0.889487870619946, 'f1': 0.8847184986595173, 'number': 371}                | 0.9807            | 0.9848         | 0.9828     | 0.9948           |
+| 0.0055        | 10.0  | 400  | 0.0291          | {'precision': 0.9905636317266003, 'recall': 0.9941131302789864, 'f1': 0.9923352069494124, 'number': 3907} | {'precision': 0.9671268902038133, 'recall': 0.9865861837692823, 'f1': 0.9767596281540504, 'number': 1491} | {'precision': 0.9976359338061466, 'recall': 0.985981308411215, 'f1': 0.991774383078731, 'number': 428}   | {'precision': 0.9025787965616046, 'recall': 0.8490566037735849, 'f1': 0.875, 'number': 371}              | 0.9804            | 0.9831         | 0.9817     | 0.9944           |
+| 0.0045        | 11.0  | 440  | 0.0292          | {'precision': 0.9915751850906306, 'recall': 0.9941131302789864, 'f1': 0.992842535787321, 'number': 3907}  | {'precision': 0.9696169088507266, 'recall': 0.9845741113346748, 'f1': 0.9770382695507488, 'number': 1491} | {'precision': 1.0, 'recall': 0.985981308411215, 'f1': 0.9929411764705882, 'number': 428}                 | {'precision': 0.8817204301075269, 'recall': 0.8840970350404312, 'f1': 0.882907133243607, 'number': 371}  | 0.9802            | 0.9847         | 0.9825     | 0.9947           |
+| 0.0042        | 12.0  | 480  | 0.0310          | {'precision': 0.9913221031138336, 'recall': 0.9941131302789864, 'f1': 0.9927156549520767, 'number': 3907} | {'precision': 0.9683794466403162, 'recall': 0.9859154929577465, 'f1': 0.9770687936191426, 'number': 1491} | {'precision': 1.0, 'recall': 0.9836448598130841, 'f1': 0.9917550058892814, 'number': 428}                | {'precision': 0.8763440860215054, 'recall': 0.8787061994609164, 'f1': 0.8775235531628534, 'number': 371} | 0.9795            | 0.9845         | 0.9820     | 0.9945           |
+| 0.0038        | 13.0  | 520  | 0.0316          | {'precision': 0.9915751850906306, 'recall': 0.9941131302789864, 'f1': 0.992842535787321, 'number': 3907}  | {'precision': 0.9652230971128609, 'recall': 0.9865861837692823, 'f1': 0.9757877280265339, 'number': 1491} | {'precision': 0.9952830188679245, 'recall': 0.985981308411215, 'f1': 0.9906103286384976, 'number': 428}  | {'precision': 0.8649350649350649, 'recall': 0.8975741239892183, 'f1': 0.8809523809523809, 'number': 371} | 0.9776            | 0.9860         | 0.9818     | 0.9944           |
+| 0.0035        | 14.0  | 560  | 0.0311          | {'precision': 0.9915751850906306, 'recall': 0.9941131302789864, 'f1': 0.992842535787321, 'number': 3907}  | {'precision': 0.9658568614576494, 'recall': 0.9865861837692823, 'f1': 0.9761114797611148, 'number': 1491} | {'precision': 1.0, 'recall': 0.985981308411215, 'f1': 0.9929411764705882, 'number': 428}                 | {'precision': 0.8790322580645161, 'recall': 0.8814016172506739, 'f1': 0.8802153432032301, 'number': 371} | 0.9791            | 0.9850         | 0.9821     | 0.9945           |
+| 0.0032        | 15.0  | 600  | 0.0312          | {'precision': 0.9915751850906306, 'recall': 0.9941131302789864, 'f1': 0.992842535787321, 'number': 3907}  | {'precision': 0.966491458607096, 'recall': 0.9865861837692823, 'f1': 0.9764354463989379, 'number': 1491}  | {'precision': 1.0, 'recall': 0.985981308411215, 'f1': 0.9929411764705882, 'number': 428}                 | {'precision': 0.8783068783068783, 'recall': 0.894878706199461, 'f1': 0.8865153538050735, 'number': 371}  | 0.9792            | 0.9858         | 0.9825     | 0.9947           |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.13.3

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