End of training

README.md

@@ -0,0 +1,77 @@
+---
+library_name: transformers
+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: 1.5422
+- Answer: {'precision': 0.8987804878048781, 'recall': 0.9020807833537332, 'f1': 0.9004276114844226, 'number': 817}
+- Header: {'precision': 0.6528925619834711, 'recall': 0.6638655462184874, 'f1': 0.6583333333333333, 'number': 119}
+- Question: {'precision': 0.8772845953002611, 'recall': 0.935933147632312, 'f1': 0.9056603773584906, 'number': 1077}
+- Overall Precision: 0.8727
+- Overall Recall: 0.9061
+- Overall F1: 0.8891
+- Overall Accuracy: 0.8153
+
+## 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 |
+|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4117        | 10.5263  | 200  | 1.1040          | {'precision': 0.8423475258918297, 'recall': 0.8959608323133414, 'f1': 0.8683274021352312, 'number': 817} | {'precision': 0.4925373134328358, 'recall': 0.5546218487394958, 'f1': 0.5217391304347825, 'number': 119}  | {'precision': 0.8823529411764706, 'recall': 0.8913649025069638, 'f1': 0.8868360277136259, 'number': 1077} | 0.8407            | 0.8733         | 0.8567     | 0.7772           |
+| 0.0494        | 21.0526  | 400  | 1.4246          | {'precision': 0.8377142857142857, 'recall': 0.8971848225214198, 'f1': 0.8664302600472813, 'number': 817} | {'precision': 0.5655737704918032, 'recall': 0.5798319327731093, 'f1': 0.5726141078838175, 'number': 119}  | {'precision': 0.8783542039355993, 'recall': 0.9117920148560817, 'f1': 0.894760820045558, 'number': 1077}  | 0.8435            | 0.8862         | 0.8643     | 0.7895           |
+| 0.0149        | 31.5789  | 600  | 1.3688          | {'precision': 0.8272827282728272, 'recall': 0.9204406364749081, 'f1': 0.8713789107763616, 'number': 817} | {'precision': 0.6436781609195402, 'recall': 0.47058823529411764, 'f1': 0.5436893203883495, 'number': 119} | {'precision': 0.8790613718411552, 'recall': 0.904363974001857, 'f1': 0.891533180778032, 'number': 1077}   | 0.8470            | 0.8852         | 0.8657     | 0.8066           |
+| 0.0074        | 42.1053  | 800  | 1.5512          | {'precision': 0.8574739281575898, 'recall': 0.9057527539779682, 'f1': 0.880952380952381, 'number': 817}  | {'precision': 0.6140350877192983, 'recall': 0.5882352941176471, 'f1': 0.6008583690987125, 'number': 119}  | {'precision': 0.8713656387665198, 'recall': 0.9182915506035283, 'f1': 0.8942133815551537, 'number': 1077} | 0.8518            | 0.8937         | 0.8722     | 0.7908           |
+| 0.004         | 52.6316  | 1000 | 1.5808          | {'precision': 0.8884892086330936, 'recall': 0.9069767441860465, 'f1': 0.8976377952755905, 'number': 817} | {'precision': 0.6239316239316239, 'recall': 0.6134453781512605, 'f1': 0.6186440677966102, 'number': 119}  | {'precision': 0.8902991840435177, 'recall': 0.9117920148560817, 'f1': 0.9009174311926607, 'number': 1077} | 0.8744            | 0.8922         | 0.8832     | 0.8017           |
+| 0.0025        | 63.1579  | 1200 | 1.6746          | {'precision': 0.8933333333333333, 'recall': 0.9020807833537332, 'f1': 0.8976857490864798, 'number': 817} | {'precision': 0.5873015873015873, 'recall': 0.6218487394957983, 'f1': 0.6040816326530611, 'number': 119}  | {'precision': 0.8998194945848376, 'recall': 0.9257195914577531, 'f1': 0.9125858123569794, 'number': 1077} | 0.8781            | 0.8982         | 0.8880     | 0.7978           |
+| 0.0014        | 73.6842  | 1400 | 1.5175          | {'precision': 0.8628841607565012, 'recall': 0.8935128518971848, 'f1': 0.8779314491882141, 'number': 817} | {'precision': 0.5833333333333334, 'recall': 0.5882352941176471, 'f1': 0.5857740585774059, 'number': 119}  | {'precision': 0.8736933797909407, 'recall': 0.9312906220984215, 'f1': 0.9015730337078652, 'number': 1077} | 0.8529            | 0.8957         | 0.8738     | 0.8073           |
+| 0.0007        | 84.2105  | 1600 | 1.5422          | {'precision': 0.8987804878048781, 'recall': 0.9020807833537332, 'f1': 0.9004276114844226, 'number': 817} | {'precision': 0.6528925619834711, 'recall': 0.6638655462184874, 'f1': 0.6583333333333333, 'number': 119}  | {'precision': 0.8772845953002611, 'recall': 0.935933147632312, 'f1': 0.9056603773584906, 'number': 1077}  | 0.8727            | 0.9061         | 0.8891     | 0.8153           |
+| 0.0007        | 94.7368  | 1800 | 1.6505          | {'precision': 0.8702380952380953, 'recall': 0.8947368421052632, 'f1': 0.8823174411587205, 'number': 817} | {'precision': 0.5703125, 'recall': 0.6134453781512605, 'f1': 0.5910931174089069, 'number': 119}           | {'precision': 0.8894927536231884, 'recall': 0.9117920148560817, 'f1': 0.9005043558000918, 'number': 1077} | 0.8620            | 0.8872         | 0.8744     | 0.8034           |
+| 0.0007        | 105.2632 | 2000 | 1.6541          | {'precision': 0.8685446009389671, 'recall': 0.9057527539779682, 'f1': 0.8867585380467345, 'number': 817} | {'precision': 0.6095238095238096, 'recall': 0.5378151260504201, 'f1': 0.5714285714285715, 'number': 119}  | {'precision': 0.8938700823421775, 'recall': 0.9071494893221913, 'f1': 0.9004608294930875, 'number': 1077} | 0.8688            | 0.8847         | 0.8767     | 0.8024           |
+| 0.0004        | 115.7895 | 2200 | 1.6276          | {'precision': 0.866902237926973, 'recall': 0.9008567931456548, 'f1': 0.8835534213685473, 'number': 817}  | {'precision': 0.64, 'recall': 0.5378151260504201, 'f1': 0.5844748858447488, 'number': 119}                | {'precision': 0.8941704035874439, 'recall': 0.9257195914577531, 'f1': 0.9096715328467153, 'number': 1077} | 0.8706            | 0.8927         | 0.8815     | 0.8082           |
+| 0.0003        | 126.3158 | 2400 | 1.6682          | {'precision': 0.8790419161676647, 'recall': 0.8984088127294981, 'f1': 0.8886198547215496, 'number': 817} | {'precision': 0.5964912280701754, 'recall': 0.5714285714285714, 'f1': 0.5836909871244635, 'number': 119}  | {'precision': 0.8928892889288929, 'recall': 0.9210770659238626, 'f1': 0.9067641681901281, 'number': 1077} | 0.8709            | 0.8912         | 0.8809     | 0.8042           |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.1+cpu
+- Datasets 3.0.0
+- Tokenizers 0.19.1

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