End of training

README.md

@@ -0,0 +1,76 @@
+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- funsd-layoutlmv3
+model-index:
+- name: new_model
+  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. -->
+
+# new_model
+
+This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5095
+- Answer: {'precision': 0.8368479467258602, 'recall': 0.9228886168910648, 'f1': 0.8777648428405123, 'number': 817}
+- Header: {'precision': 0.5333333333333333, 'recall': 0.5378151260504201, 'f1': 0.5355648535564853, 'number': 119}
+- Question: {'precision': 0.8907407407407407, 'recall': 0.89322191272052, 'f1': 0.8919796012980993, 'number': 1077}
+- Overall Precision: 0.8472
+- Overall Recall: 0.8843
+- Overall F1: 0.8653
+- Overall Accuracy: 0.7922
+
+## 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: 5e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 2500
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                     | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.1542        | 3.51  | 200  | 0.2369          | {'precision': 0.7753846153846153, 'recall': 0.9253365973072215, 'f1': 0.8437500000000001, 'number': 817} | {'precision': 0.4166666666666667, 'recall': 0.21008403361344538, 'f1': 0.2793296089385475, 'number': 119}  | {'precision': 0.8449744463373083, 'recall': 0.9210770659238626, 'f1': 0.8813860506441582, 'number': 1077} | 0.8026            | 0.8808         | 0.8399     | 0.7940           |
+| 0.0493        | 7.02  | 400  | 0.2901          | {'precision': 0.8056133056133056, 'recall': 0.9485924112607099, 'f1': 0.8712759977515458, 'number': 817} | {'precision': 0.45614035087719296, 'recall': 0.4369747899159664, 'f1': 0.44635193133047213, 'number': 119} | {'precision': 0.888673765730881, 'recall': 0.8523676880222841, 'f1': 0.8701421800947867, 'number': 1077}  | 0.8274            | 0.8669         | 0.8467     | 0.8010           |
+| 0.0214        | 10.53 | 600  | 0.3571          | {'precision': 0.8139013452914798, 'recall': 0.8886168910648715, 'f1': 0.8496196606202459, 'number': 817} | {'precision': 0.4186046511627907, 'recall': 0.6050420168067226, 'f1': 0.4948453608247423, 'number': 119}   | {'precision': 0.8652946679139383, 'recall': 0.8588672237697307, 'f1': 0.8620689655172413, 'number': 1077} | 0.8078            | 0.8559         | 0.8312     | 0.7907           |
+| 0.0112        | 14.04 | 800  | 0.4018          | {'precision': 0.8484455958549223, 'recall': 0.8017135862913096, 'f1': 0.8244178728760226, 'number': 817} | {'precision': 0.45, 'recall': 0.6050420168067226, 'f1': 0.5161290322580645, 'number': 119}                 | {'precision': 0.8385689354275742, 'recall': 0.8922934076137419, 'f1': 0.8645973909131804, 'number': 1077} | 0.8123            | 0.8385         | 0.8252     | 0.7872           |
+| 0.0058        | 17.54 | 1000 | 0.4571          | {'precision': 0.8466666666666667, 'recall': 0.9326805385556916, 'f1': 0.8875946418171229, 'number': 817} | {'precision': 0.6621621621621622, 'recall': 0.4117647058823529, 'f1': 0.5077720207253885, 'number': 119}   | {'precision': 0.8809738503155996, 'recall': 0.9071494893221913, 'f1': 0.8938700823421775, 'number': 1077} | 0.8584            | 0.8882         | 0.8730     | 0.7944           |
+| 0.0031        | 21.05 | 1200 | 0.4573          | {'precision': 0.8208469055374593, 'recall': 0.9253365973072215, 'f1': 0.8699654775604143, 'number': 817} | {'precision': 0.5727272727272728, 'recall': 0.5294117647058824, 'f1': 0.5502183406113538, 'number': 119}   | {'precision': 0.8799266727772685, 'recall': 0.8913649025069638, 'f1': 0.8856088560885608, 'number': 1077} | 0.8384            | 0.8838         | 0.8605     | 0.7891           |
+| 0.0019        | 24.56 | 1400 | 0.4631          | {'precision': 0.8372352285395763, 'recall': 0.9192166462668299, 'f1': 0.8763127187864644, 'number': 817} | {'precision': 0.559322033898305, 'recall': 0.5546218487394958, 'f1': 0.5569620253164557, 'number': 119}    | {'precision': 0.8864059590316573, 'recall': 0.8839368616527391, 'f1': 0.8851696885169689, 'number': 1077} | 0.8468            | 0.8788         | 0.8625     | 0.7977           |
+| 0.0011        | 28.07 | 1600 | 0.5118          | {'precision': 0.838074398249453, 'recall': 0.9375764993880049, 'f1': 0.8850375505488157, 'number': 817}  | {'precision': 0.5862068965517241, 'recall': 0.5714285714285714, 'f1': 0.5787234042553192, 'number': 119}   | {'precision': 0.885972850678733, 'recall': 0.9090064995357474, 'f1': 0.8973418881759853, 'number': 1077}  | 0.8492            | 0.9006         | 0.8742     | 0.7970           |
+| 0.0006        | 31.58 | 1800 | 0.4786          | {'precision': 0.8383500557413601, 'recall': 0.9204406364749081, 'f1': 0.8774795799299884, 'number': 817} | {'precision': 0.6, 'recall': 0.5546218487394958, 'f1': 0.5764192139737991, 'number': 119}                  | {'precision': 0.8851412944393802, 'recall': 0.9015784586815228, 'f1': 0.8932842686292549, 'number': 1077} | 0.8503            | 0.8887         | 0.8691     | 0.7942           |
+| 0.0004        | 35.09 | 2000 | 0.4959          | {'precision': 0.8338945005611672, 'recall': 0.9094247246022031, 'f1': 0.870023419203747, 'number': 817}  | {'precision': 0.5172413793103449, 'recall': 0.6302521008403361, 'f1': 0.5681818181818182, 'number': 119}   | {'precision': 0.8833333333333333, 'recall': 0.8857938718662952, 'f1': 0.8845618915159944, 'number': 1077} | 0.8374            | 0.8803         | 0.8583     | 0.7919           |
+| 0.0003        | 38.6  | 2200 | 0.5023          | {'precision': 0.8292682926829268, 'recall': 0.9155446756425949, 'f1': 0.8702734147760327, 'number': 817} | {'precision': 0.5470085470085471, 'recall': 0.5378151260504201, 'f1': 0.5423728813559322, 'number': 119}   | {'precision': 0.8771610555050046, 'recall': 0.8950789229340761, 'f1': 0.8860294117647057, 'number': 1077} | 0.8385            | 0.8823         | 0.8598     | 0.7948           |
+| 0.0002        | 42.11 | 2400 | 0.5095          | {'precision': 0.8368479467258602, 'recall': 0.9228886168910648, 'f1': 0.8777648428405123, 'number': 817} | {'precision': 0.5333333333333333, 'recall': 0.5378151260504201, 'f1': 0.5355648535564853, 'number': 119}   | {'precision': 0.8907407407407407, 'recall': 0.89322191272052, 'f1': 0.8919796012980993, 'number': 1077}   | 0.8472            | 0.8843         | 0.8653     | 0.7922           |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.1.0.dev20230810
+- Datasets 2.14.4
+- Tokenizers 0.11.0

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