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: 1.5687
+- Answer: {'precision': 0.8635294117647059, 'recall': 0.8984088127294981, 'f1': 0.8806238752249551, 'number': 817}
+- Header: {'precision': 0.6781609195402298, 'recall': 0.4957983193277311, 'f1': 0.5728155339805825, 'number': 119}
+- Question: {'precision': 0.8912655971479501, 'recall': 0.9285051067780873, 'f1': 0.9095043201455207, 'number': 1077}
+- Overall Precision: 0.8708
+- Overall Recall: 0.8907
+- Overall F1: 0.8806
+- Overall Accuracy: 0.8098
+
+## 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 |
+|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4119        | 10.5263  | 200  | 1.0913          | {'precision': 0.8155555555555556, 'recall': 0.8984088127294981, 'f1': 0.8549796156086196, 'number': 817} | {'precision': 0.5523809523809524, 'recall': 0.48739495798319327, 'f1': 0.5178571428571428, 'number': 119} | {'precision': 0.8904363974001857, 'recall': 0.8904363974001857, 'f1': 0.8904363974001857, 'number': 1077} | 0.8410            | 0.8698         | 0.8552     | 0.7742           |
+| 0.0512        | 21.0526  | 400  | 1.3167          | {'precision': 0.8363636363636363, 'recall': 0.9008567931456548, 'f1': 0.8674130819092517, 'number': 817} | {'precision': 0.6160714285714286, 'recall': 0.5798319327731093, 'f1': 0.5974025974025975, 'number': 119}  | {'precision': 0.8839122486288848, 'recall': 0.8978644382544104, 'f1': 0.8908337171810224, 'number': 1077} | 0.8495            | 0.8803         | 0.8646     | 0.7919           |
+| 0.0159        | 31.5789  | 600  | 1.4418          | {'precision': 0.8539458186101295, 'recall': 0.8873929008567931, 'f1': 0.8703481392557022, 'number': 817} | {'precision': 0.5913978494623656, 'recall': 0.46218487394957986, 'f1': 0.5188679245283019, 'number': 119} | {'precision': 0.8617113223854796, 'recall': 0.9257195914577531, 'f1': 0.8925693822739481, 'number': 1077} | 0.8466            | 0.8828         | 0.8643     | 0.7994           |
+| 0.0085        | 42.1053  | 800  | 1.3252          | {'precision': 0.8408839779005525, 'recall': 0.9314565483476133, 'f1': 0.8838559814169571, 'number': 817} | {'precision': 0.6176470588235294, 'recall': 0.5294117647058824, 'f1': 0.5701357466063349, 'number': 119}  | {'precision': 0.8946412352406903, 'recall': 0.914577530176416, 'f1': 0.9044995408631772, 'number': 1077}  | 0.8582            | 0.8987         | 0.8779     | 0.8077           |
+| 0.0035        | 52.6316  | 1000 | 1.4334          | {'precision': 0.8364269141531323, 'recall': 0.8824969400244798, 'f1': 0.8588445503275759, 'number': 817} | {'precision': 0.6, 'recall': 0.5798319327731093, 'f1': 0.5897435897435898, 'number': 119}                 | {'precision': 0.8983516483516484, 'recall': 0.9108635097493036, 'f1': 0.9045643153526972, 'number': 1077} | 0.8560            | 0.8798         | 0.8677     | 0.7988           |
+| 0.0031        | 63.1579  | 1200 | 1.3464          | {'precision': 0.8569780853517878, 'recall': 0.9094247246022031, 'f1': 0.8824228028503563, 'number': 817} | {'precision': 0.6836734693877551, 'recall': 0.5630252100840336, 'f1': 0.6175115207373272, 'number': 119}  | {'precision': 0.8909090909090909, 'recall': 0.9099350046425255, 'f1': 0.90032154340836, 'number': 1077}   | 0.8668            | 0.8892         | 0.8779     | 0.8164           |
+| 0.0017        | 73.6842  | 1400 | 1.5019          | {'precision': 0.883054892601432, 'recall': 0.9057527539779682, 'f1': 0.8942598187311178, 'number': 817}  | {'precision': 0.6565656565656566, 'recall': 0.5462184873949579, 'f1': 0.5963302752293578, 'number': 119}  | {'precision': 0.8922528940338379, 'recall': 0.9303621169916435, 'f1': 0.9109090909090909, 'number': 1077} | 0.8772            | 0.8977         | 0.8873     | 0.8110           |
+| 0.0008        | 84.2105  | 1600 | 1.5955          | {'precision': 0.8760631834750912, 'recall': 0.8824969400244798, 'f1': 0.8792682926829267, 'number': 817} | {'precision': 0.5925925925925926, 'recall': 0.5378151260504201, 'f1': 0.5638766519823789, 'number': 119}  | {'precision': 0.8853046594982079, 'recall': 0.9173630454967502, 'f1': 0.9010487916096672, 'number': 1077} | 0.8661            | 0.8808         | 0.8734     | 0.8084           |
+| 0.0006        | 94.7368  | 1800 | 1.5931          | {'precision': 0.8713592233009708, 'recall': 0.8788249694002448, 'f1': 0.8750761730652041, 'number': 817} | {'precision': 0.6122448979591837, 'recall': 0.5042016806722689, 'f1': 0.5529953917050692, 'number': 119}  | {'precision': 0.8834519572953736, 'recall': 0.9220055710306406, 'f1': 0.9023171285779192, 'number': 1077} | 0.8656            | 0.8798         | 0.8726     | 0.7973           |
+| 0.0006        | 105.2632 | 2000 | 1.5735          | {'precision': 0.8676122931442081, 'recall': 0.8984088127294981, 'f1': 0.8827420324714371, 'number': 817} | {'precision': 0.6176470588235294, 'recall': 0.5294117647058824, 'f1': 0.5701357466063349, 'number': 119}  | {'precision': 0.8862222222222222, 'recall': 0.9257195914577531, 'f1': 0.9055404178019981, 'number': 1077} | 0.8654            | 0.8912         | 0.8781     | 0.8002           |
+| 0.001         | 115.7895 | 2200 | 1.5605          | {'precision': 0.8543352601156069, 'recall': 0.9045287637698899, 'f1': 0.8787158145065399, 'number': 817} | {'precision': 0.6559139784946236, 'recall': 0.5126050420168067, 'f1': 0.5754716981132076, 'number': 119}  | {'precision': 0.8935978358881875, 'recall': 0.9201485608170845, 'f1': 0.9066788655077767, 'number': 1077} | 0.8665            | 0.8897         | 0.8779     | 0.8073           |
+| 0.0004        | 126.3158 | 2400 | 1.5687          | {'precision': 0.8635294117647059, 'recall': 0.8984088127294981, 'f1': 0.8806238752249551, 'number': 817} | {'precision': 0.6781609195402298, 'recall': 0.4957983193277311, 'f1': 0.5728155339805825, 'number': 119}  | {'precision': 0.8912655971479501, 'recall': 0.9285051067780873, 'f1': 0.9095043201455207, 'number': 1077} | 0.8708            | 0.8907         | 0.8806     | 0.8098           |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

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