End of training

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- funsd-layoutlmv3
+model-index:
+- name: lilt-en-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. -->
+
+# lilt-en-funsd
+
+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: 1.4114
+- Answer: {'precision': 0.8497175141242937, 'recall': 0.9204406364749081, 'f1': 0.8836662749706228, 'number': 817}
+- Header: {'precision': 0.6534653465346535, 'recall': 0.5546218487394958, 'f1': 0.6000000000000001, 'number': 119}
+- Question: {'precision': 0.8935018050541517, 'recall': 0.9192200557103064, 'f1': 0.9061784897025171, 'number': 1077}
+- Overall Precision: 0.8634
+- Overall Recall: 0.8982
+- Overall F1: 0.8804
+- Overall Accuracy: 0.8253
+
+## 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
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                     | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.575         | 5.26  | 200  | 0.8531          | {'precision': 0.7890295358649789, 'recall': 0.9155446756425949, 'f1': 0.8475920679886686, 'number': 817} | {'precision': 0.5416666666666666, 'recall': 0.3277310924369748, 'f1': 0.40837696335078527, 'number': 119}  | {'precision': 0.8611599297012302, 'recall': 0.9099350046425255, 'f1': 0.8848758465011286, 'number': 1077} | 0.8188            | 0.8778         | 0.8473     | 0.7926           |
+| 0.119         | 10.53 | 400  | 1.1026          | {'precision': 0.8278688524590164, 'recall': 0.8653610771113831, 'f1': 0.846199880311191, 'number': 817}  | {'precision': 0.5813953488372093, 'recall': 0.42016806722689076, 'f1': 0.48780487804878053, 'number': 119} | {'precision': 0.856655290102389, 'recall': 0.9322191272051996, 'f1': 0.8928412627834592, 'number': 1077}  | 0.8338            | 0.8748         | 0.8538     | 0.8124           |
+| 0.0411        | 15.79 | 600  | 1.2238          | {'precision': 0.8713942307692307, 'recall': 0.8873929008567931, 'f1': 0.8793208004851426, 'number': 817} | {'precision': 0.552, 'recall': 0.5798319327731093, 'f1': 0.5655737704918032, 'number': 119}                | {'precision': 0.8669527896995708, 'recall': 0.9377901578458682, 'f1': 0.9009812667261373, 'number': 1077} | 0.8501            | 0.8962         | 0.8726     | 0.8131           |
+| 0.0186        | 21.05 | 800  | 1.2807          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.5447154471544715, 'recall': 0.5630252100840336, 'f1': 0.5537190082644629, 'number': 119}   | {'precision': 0.8921389396709324, 'recall': 0.9062209842154132, 'f1': 0.8991248272685398, 'number': 1077} | 0.8586            | 0.8867         | 0.8724     | 0.8162           |
+| 0.0098        | 26.32 | 1000 | 1.3494          | {'precision': 0.852233676975945, 'recall': 0.9106487148102815, 'f1': 0.8804733727810652, 'number': 817}  | {'precision': 0.5511811023622047, 'recall': 0.5882352941176471, 'f1': 0.5691056910569106, 'number': 119}   | {'precision': 0.8794964028776978, 'recall': 0.9080779944289693, 'f1': 0.8935587026039287, 'number': 1077} | 0.8485            | 0.8902         | 0.8688     | 0.8039           |
+| 0.0068        | 31.58 | 1200 | 1.3878          | {'precision': 0.8495475113122172, 'recall': 0.9192166462668299, 'f1': 0.8830099941211051, 'number': 817} | {'precision': 0.5565217391304348, 'recall': 0.5378151260504201, 'f1': 0.547008547008547, 'number': 119}    | {'precision': 0.899624765478424, 'recall': 0.8904363974001857, 'f1': 0.8950069995333644, 'number': 1077}  | 0.8591            | 0.8813         | 0.8700     | 0.8140           |
+| 0.0056        | 36.84 | 1400 | 1.4679          | {'precision': 0.8338833883388339, 'recall': 0.9277845777233782, 'f1': 0.8783314020857474, 'number': 817} | {'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119}    | {'precision': 0.8971000935453695, 'recall': 0.8904363974001857, 'f1': 0.8937558247903075, 'number': 1077} | 0.8569            | 0.8862         | 0.8713     | 0.8117           |
+| 0.0033        | 42.11 | 1600 | 1.3959          | {'precision': 0.8463276836158192, 'recall': 0.9167686658506732, 'f1': 0.8801410105757932, 'number': 817} | {'precision': 0.5833333333333334, 'recall': 0.5882352941176471, 'f1': 0.5857740585774059, 'number': 119}   | {'precision': 0.8939114391143912, 'recall': 0.8997214484679665, 'f1': 0.8968070337806571, 'number': 1077} | 0.8559            | 0.8882         | 0.8718     | 0.8177           |
+| 0.0013        | 47.37 | 1800 | 1.4114          | {'precision': 0.8497175141242937, 'recall': 0.9204406364749081, 'f1': 0.8836662749706228, 'number': 817} | {'precision': 0.6534653465346535, 'recall': 0.5546218487394958, 'f1': 0.6000000000000001, 'number': 119}   | {'precision': 0.8935018050541517, 'recall': 0.9192200557103064, 'f1': 0.9061784897025171, 'number': 1077} | 0.8634            | 0.8982         | 0.8804     | 0.8253           |
+| 0.001         | 52.63 | 2000 | 1.3795          | {'precision': 0.8584795321637427, 'recall': 0.8984088127294981, 'f1': 0.8779904306220095, 'number': 817} | {'precision': 0.6306306306306306, 'recall': 0.5882352941176471, 'f1': 0.6086956521739131, 'number': 119}   | {'precision': 0.8965201465201466, 'recall': 0.9090064995357474, 'f1': 0.9027201475334256, 'number': 1077} | 0.8664            | 0.8857         | 0.8760     | 0.8339           |
+| 0.0007        | 57.89 | 2200 | 1.4095          | {'precision': 0.8586206896551725, 'recall': 0.9143206854345165, 'f1': 0.8855957320687612, 'number': 817} | {'precision': 0.6055045871559633, 'recall': 0.5546218487394958, 'f1': 0.5789473684210525, 'number': 119}   | {'precision': 0.8887884267631103, 'recall': 0.9127205199628597, 'f1': 0.9005955107650022, 'number': 1077} | 0.8614            | 0.8922         | 0.8765     | 0.8216           |
+| 0.0006        | 63.16 | 2400 | 1.4001          | {'precision': 0.8577981651376146, 'recall': 0.9155446756425949, 'f1': 0.8857312018946123, 'number': 817} | {'precision': 0.6216216216216216, 'recall': 0.5798319327731093, 'f1': 0.6000000000000001, 'number': 119}   | {'precision': 0.895644283121597, 'recall': 0.9164345403899722, 'f1': 0.9059201468563561, 'number': 1077}  | 0.8652            | 0.8962         | 0.8804     | 0.8282           |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 1.13.0+cu117
+- Datasets 2.11.0
+- Tokenizers 0.13.2

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