End of training

README.md

@@ -0,0 +1,76 @@
+---
+license: mit
+base_model: SCUT-DLVCLab/lilt-roberta-en-base
+tags:
+- generated_from_trainer
+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 an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6470
+- Answer: {'precision': 0.8841607565011821, 'recall': 0.9155446756425949, 'f1': 0.899579073962718, 'number': 817}
+- Header: {'precision': 0.6574074074074074, 'recall': 0.5966386554621849, 'f1': 0.6255506607929515, 'number': 119}
+- Question: {'precision': 0.900804289544236, 'recall': 0.935933147632312, 'f1': 0.9180327868852459, 'number': 1077}
+- Overall Precision: 0.8813
+- Overall Recall: 0.9076
+- Overall F1: 0.8943
+- Overall Accuracy: 0.8114
+
+## 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: 8
+- eval_batch_size: 8
+- 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4017        | 10.53  | 200  | 1.0898          | {'precision': 0.8257491675915649, 'recall': 0.9106487148102815, 'f1': 0.8661233993015134, 'number': 817} | {'precision': 0.49166666666666664, 'recall': 0.4957983193277311, 'f1': 0.4937238493723849, 'number': 119} | {'precision': 0.8689591078066915, 'recall': 0.8681522748375116, 'f1': 0.8685555039479796, 'number': 1077} | 0.8288            | 0.8634         | 0.8457     | 0.7811           |
+| 0.0453        | 21.05  | 400  | 1.1928          | {'precision': 0.8400900900900901, 'recall': 0.9130966952264382, 'f1': 0.8750733137829912, 'number': 817} | {'precision': 0.5575221238938053, 'recall': 0.5294117647058824, 'f1': 0.543103448275862, 'number': 119}   | {'precision': 0.8885869565217391, 'recall': 0.9108635097493036, 'f1': 0.8995873452544704, 'number': 1077} | 0.8504            | 0.8892         | 0.8694     | 0.8044           |
+| 0.0148        | 31.58  | 600  | 1.3654          | {'precision': 0.8174946004319654, 'recall': 0.9265605875152999, 'f1': 0.8686173264486519, 'number': 817} | {'precision': 0.5391304347826087, 'recall': 0.5210084033613446, 'f1': 0.52991452991453, 'number': 119}    | {'precision': 0.8866171003717472, 'recall': 0.8857938718662952, 'f1': 0.8862052949372968, 'number': 1077} | 0.8375            | 0.8808         | 0.8586     | 0.8086           |
+| 0.0073        | 42.11  | 800  | 1.4733          | {'precision': 0.8653395784543325, 'recall': 0.9045287637698899, 'f1': 0.8845002992220227, 'number': 817} | {'precision': 0.6597938144329897, 'recall': 0.5378151260504201, 'f1': 0.5925925925925926, 'number': 119}  | {'precision': 0.8881057268722466, 'recall': 0.935933147632312, 'f1': 0.9113924050632911, 'number': 1077}  | 0.8682            | 0.8997         | 0.8836     | 0.8111           |
+| 0.008         | 52.63  | 1000 | 1.6415          | {'precision': 0.864963503649635, 'recall': 0.8702570379436965, 'f1': 0.8676021964612569, 'number': 817}  | {'precision': 0.5636363636363636, 'recall': 0.5210084033613446, 'f1': 0.5414847161572053, 'number': 119}  | {'precision': 0.8630490956072352, 'recall': 0.9303621169916435, 'f1': 0.8954423592493298, 'number': 1077} | 0.8481            | 0.8818         | 0.8646     | 0.7959           |
+| 0.0026        | 63.16  | 1200 | 1.5876          | {'precision': 0.8830049261083743, 'recall': 0.8776009791921665, 'f1': 0.8802946593001842, 'number': 817} | {'precision': 0.5564516129032258, 'recall': 0.5798319327731093, 'f1': 0.5679012345679013, 'number': 119}  | {'precision': 0.8905693950177936, 'recall': 0.9294336118848654, 'f1': 0.9095865515674693, 'number': 1077} | 0.8675            | 0.8877         | 0.8775     | 0.8032           |
+| 0.0013        | 73.68  | 1400 | 1.5427          | {'precision': 0.8733572281959379, 'recall': 0.8947368421052632, 'f1': 0.8839177750906893, 'number': 817} | {'precision': 0.6271186440677966, 'recall': 0.6218487394957983, 'f1': 0.6244725738396625, 'number': 119}  | {'precision': 0.9108455882352942, 'recall': 0.9201485608170845, 'f1': 0.9154734411085451, 'number': 1077} | 0.8791            | 0.8922         | 0.8856     | 0.8115           |
+| 0.0012        | 84.21  | 1600 | 1.5540          | {'precision': 0.8790035587188612, 'recall': 0.9069767441860465, 'f1': 0.8927710843373493, 'number': 817} | {'precision': 0.6216216216216216, 'recall': 0.5798319327731093, 'f1': 0.6000000000000001, 'number': 119}  | {'precision': 0.9066543438077634, 'recall': 0.9108635097493036, 'f1': 0.9087540528022232, 'number': 1077} | 0.8797            | 0.8897         | 0.8847     | 0.8096           |
+| 0.0007        | 94.74  | 1800 | 1.6470          | {'precision': 0.8841607565011821, 'recall': 0.9155446756425949, 'f1': 0.899579073962718, 'number': 817}  | {'precision': 0.6574074074074074, 'recall': 0.5966386554621849, 'f1': 0.6255506607929515, 'number': 119}  | {'precision': 0.900804289544236, 'recall': 0.935933147632312, 'f1': 0.9180327868852459, 'number': 1077}   | 0.8813            | 0.9076         | 0.8943     | 0.8114           |
+| 0.0004        | 105.26 | 2000 | 1.6446          | {'precision': 0.877906976744186, 'recall': 0.9241126070991432, 'f1': 0.9004174120453191, 'number': 817}  | {'precision': 0.6739130434782609, 'recall': 0.5210084033613446, 'f1': 0.5876777251184835, 'number': 119}  | {'precision': 0.8982142857142857, 'recall': 0.9340761374187558, 'f1': 0.915794264906691, 'number': 1077}  | 0.8798            | 0.9056         | 0.8925     | 0.8196           |
+| 0.0003        | 115.79 | 2200 | 1.6375          | {'precision': 0.8638443935926774, 'recall': 0.9241126070991432, 'f1': 0.892962743938498, 'number': 817}  | {'precision': 0.7375, 'recall': 0.4957983193277311, 'f1': 0.592964824120603, 'number': 119}               | {'precision': 0.8999098286744815, 'recall': 0.9266480965645311, 'f1': 0.9130832570905763, 'number': 1077} | 0.8783            | 0.9001         | 0.8891     | 0.8225           |
+| 0.0003        | 126.32 | 2400 | 1.6489          | {'precision': 0.8751458576429405, 'recall': 0.9179926560587516, 'f1': 0.8960573476702508, 'number': 817} | {'precision': 0.6559139784946236, 'recall': 0.5126050420168067, 'f1': 0.5754716981132076, 'number': 119}  | {'precision': 0.9026149684400361, 'recall': 0.9294336118848654, 'f1': 0.9158279963403477, 'number': 1077} | 0.8800            | 0.9001         | 0.8900     | 0.8166           |
+
+
+### Framework versions
+
+- Transformers 4.38.0
+- Pytorch 2.1.0+cu121
+- Datasets 2.17.1
+- Tokenizers 0.15.2

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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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+}