End of training

README.md

@@ -0,0 +1,73 @@
+---
+license: cc-by-nc-sa-4.0
+tags:
+- generated_from_trainer
+model-index:
+- name: layoutlmv3-triplet
+  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-triplet
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0174
+- Item: {'precision': 0.946751863684771, 'recall': 0.9348054679284963, 'f1': 0.9407407407407407, 'number': 951}
+- Aption: {'precision': 0.9266211604095563, 'recall': 0.9225280326197758, 'f1': 0.9245700664055849, 'number': 2943}
+- Ootnote: {'precision': 0.841726618705036, 'recall': 0.8068965517241379, 'f1': 0.823943661971831, 'number': 145}
+- Ormula: {'precision': 0.9741568112133158, 'recall': 0.9754385964912281, 'f1': 0.9747972824895901, 'number': 2280}
+- Overall Precision: 0.9450
+- Overall Recall: 0.9408
+- Overall F1: 0.9429
+- Overall Accuracy: 0.9980
+
+## 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: 8
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Item                                                                                                     | Aption                                                                                                    | Ootnote                                                                                                  | Ormula                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0169        | 1.0   | 8507  | 0.0149          | {'precision': 0.8774703557312253, 'recall': 0.9337539432176656, 'f1': 0.9047376464595008, 'number': 951} | {'precision': 0.8646690813302601, 'recall': 0.8922867821950391, 'f1': 0.8782608695652173, 'number': 2943} | {'precision': 0.643312101910828, 'recall': 0.696551724137931, 'f1': 0.6688741721854304, 'number': 145}   | {'precision': 0.951314088754847, 'recall': 0.968421052631579, 'f1': 0.9597913497065856, 'number': 2280}   | 0.8921            | 0.9215         | 0.9066     | 0.9970           |
+| 0.0073        | 2.0   | 17014 | 0.0120          | {'precision': 0.9236326109391125, 'recall': 0.9411146161934806, 'f1': 0.9322916666666667, 'number': 951} | {'precision': 0.9089376053962901, 'recall': 0.9157322460074754, 'f1': 0.9123222748815166, 'number': 2943} | {'precision': 0.7913669064748201, 'recall': 0.7586206896551724, 'f1': 0.7746478873239436, 'number': 145} | {'precision': 0.9669708822251195, 'recall': 0.9758771929824561, 'f1': 0.9714036236629557, 'number': 2280} | 0.9296            | 0.9376         | 0.9336     | 0.9978           |
+| 0.0058        | 3.0   | 25521 | 0.0126          | {'precision': 0.9508379888268157, 'recall': 0.8948475289169295, 'f1': 0.9219934994582882, 'number': 951} | {'precision': 0.9105662983425414, 'recall': 0.8960244648318043, 'f1': 0.9032368556259633, 'number': 2943} | {'precision': 0.8, 'recall': 0.7724137931034483, 'f1': 0.7859649122807018, 'number': 145}                | {'precision': 0.9717314487632509, 'recall': 0.9649122807017544, 'f1': 0.9683098591549295, 'number': 2280} | 0.9362            | 0.9179         | 0.9270     | 0.9978           |
+| 0.0039        | 4.0   | 34028 | 0.0123          | {'precision': 0.9646522234891676, 'recall': 0.889589905362776, 'f1': 0.925601750547046, 'number': 951}   | {'precision': 0.913656690746474, 'recall': 0.9024804621134896, 'f1': 0.908034188034188, 'number': 2943}   | {'precision': 0.738255033557047, 'recall': 0.7586206896551724, 'f1': 0.7482993197278912, 'number': 145}  | {'precision': 0.9753086419753086, 'recall': 0.9701754385964912, 'f1': 0.9727352682497802, 'number': 2280} | 0.9392            | 0.9217         | 0.9304     | 0.9978           |
+| 0.0029        | 5.0   | 42535 | 0.0133          | {'precision': 0.9404761904761905, 'recall': 0.9137749737118822, 'f1': 0.9269333333333334, 'number': 951} | {'precision': 0.9182130584192439, 'recall': 0.90791709140333, 'f1': 0.9130360498889458, 'number': 2943}   | {'precision': 0.8273381294964028, 'recall': 0.7931034482758621, 'f1': 0.8098591549295774, 'number': 145} | {'precision': 0.9727472527472527, 'recall': 0.9706140350877193, 'f1': 0.9716794731064764, 'number': 2280} | 0.9393            | 0.9288         | 0.9340     | 0.9979           |
+| 0.0022        | 6.0   | 51042 | 0.0148          | {'precision': 0.9139896373056995, 'recall': 0.9274447949526814, 'f1': 0.9206680584551148, 'number': 951} | {'precision': 0.9104477611940298, 'recall': 0.9119945633707102, 'f1': 0.911220505856391, 'number': 2943}  | {'precision': 0.8226950354609929, 'recall': 0.8, 'f1': 0.8111888111888113, 'number': 145}                | {'precision': 0.9750765194578049, 'recall': 0.9780701754385965, 'f1': 0.976571053207795, 'number': 2280}  | 0.9323            | 0.9356         | 0.9340     | 0.9976           |
+| 0.0016        | 7.0   | 59549 | 0.0170          | {'precision': 0.9418729817007535, 'recall': 0.9200841219768665, 'f1': 0.9308510638297872, 'number': 951} | {'precision': 0.9165808444902163, 'recall': 0.9072375127420998, 'f1': 0.9118852459016393, 'number': 2943} | {'precision': 0.8405797101449275, 'recall': 0.8, 'f1': 0.8197879858657243, 'number': 145}                | {'precision': 0.9752102700309871, 'recall': 0.9662280701754385, 'f1': 0.9706983917162371, 'number': 2280} | 0.9399            | 0.9280         | 0.9339     | 0.9977           |
+| 0.0013        | 8.0   | 68056 | 0.0187          | {'precision': 0.9455709711846318, 'recall': 0.9316508937960042, 'f1': 0.9385593220338984, 'number': 951} | {'precision': 0.9238387978142076, 'recall': 0.9191301393136255, 'f1': 0.9214784534150912, 'number': 2943} | {'precision': 0.9047619047619048, 'recall': 0.7862068965517242, 'f1': 0.8413284132841328, 'number': 145} | {'precision': 0.9723562966213252, 'recall': 0.9719298245614035, 'f1': 0.9721430138188198, 'number': 2280} | 0.9443            | 0.9370         | 0.9407     | 0.9979           |
+| 0.0009        | 9.0   | 76563 | 0.0169          | {'precision': 0.9375, 'recall': 0.9305993690851735, 'f1': 0.9340369393139841, 'number': 951}             | {'precision': 0.9234449760765551, 'recall': 0.9181107713217805, 'f1': 0.9207701482364968, 'number': 2943} | {'precision': 0.8656716417910447, 'recall': 0.8, 'f1': 0.8315412186379928, 'number': 145}                | {'precision': 0.9750328515111695, 'recall': 0.9763157894736842, 'f1': 0.9756738987508219, 'number': 2280} | 0.9431            | 0.9383         | 0.9407     | 0.9979           |
+| 0.0008        | 10.0  | 85070 | 0.0174          | {'precision': 0.946751863684771, 'recall': 0.9348054679284963, 'f1': 0.9407407407407407, 'number': 951}  | {'precision': 0.9266211604095563, 'recall': 0.9225280326197758, 'f1': 0.9245700664055849, 'number': 2943} | {'precision': 0.841726618705036, 'recall': 0.8068965517241379, 'f1': 0.823943661971831, 'number': 145}   | {'precision': 0.9741568112133158, 'recall': 0.9754385964912281, 'f1': 0.9747972824895901, 'number': 2280} | 0.9450            | 0.9408         | 0.9429     | 0.9980           |
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.12.1
+- Datasets 2.9.0
+- Tokenizers 0.13.2

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preprocessor_config.json

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+{
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+  "do_normalize": true,
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+  "feature_extractor_type": "LayoutLMv3FeatureExtractor",
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+  "image_processor_type": "LayoutLMv3ImageProcessor",
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special_tokens_map.json

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+{
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tokenizer_config.json

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+}