End of training

README.md

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+---
+license: mit
+base_model: SCUT-DLVCLab/lilt-roberta-en-base
+tags:
+- generated_from_trainer
+model-index:
+- name: lilT_fintuning
+  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_fintuning
+
+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: 1.6381
+- Answer: {'precision': 0.8744075829383886, 'recall': 0.9033047735618115, 'f1': 0.8886213124623721, 'number': 817}
+- Header: {'precision': 0.6261682242990654, 'recall': 0.5630252100840336, 'f1': 0.5929203539823009, 'number': 119}
+- Question: {'precision': 0.8998194945848376, 'recall': 0.9257195914577531, 'f1': 0.9125858123569794, 'number': 1077}
+- Overall Precision: 0.8752
+- Overall Recall: 0.8952
+- Overall F1: 0.8851
+- Overall Accuracy: 0.8174
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4236        | 10.53  | 200  | 0.9243          | {'precision': 0.8401360544217688, 'recall': 0.9069767441860465, 'f1': 0.872277810476751, 'number': 817}  | {'precision': 0.5333333333333333, 'recall': 0.40336134453781514, 'f1': 0.45933014354066987, 'number': 119} | {'precision': 0.8789571694599627, 'recall': 0.8765088207985144, 'f1': 0.8777312877731288, 'number': 1077} | 0.8470            | 0.8609         | 0.8539     | 0.8079           |
+| 0.0472        | 21.05  | 400  | 1.2753          | {'precision': 0.8249721293199554, 'recall': 0.9057527539779682, 'f1': 0.8634772462077013, 'number': 817} | {'precision': 0.5, 'recall': 0.5798319327731093, 'f1': 0.5369649805447471, 'number': 119}                  | {'precision': 0.8778195488721805, 'recall': 0.8672237697307336, 'f1': 0.8724894908921065, 'number': 1077} | 0.8304            | 0.8659         | 0.8478     | 0.7910           |
+| 0.014         | 31.58  | 600  | 1.3381          | {'precision': 0.8335233751425314, 'recall': 0.8947368421052632, 'f1': 0.8630460448642266, 'number': 817} | {'precision': 0.6292134831460674, 'recall': 0.47058823529411764, 'f1': 0.5384615384615384, 'number': 119}  | {'precision': 0.8754416961130742, 'recall': 0.9201485608170845, 'f1': 0.8972385694884564, 'number': 1077} | 0.8475            | 0.8833         | 0.8650     | 0.8046           |
+| 0.0063        | 42.11  | 800  | 1.4519          | {'precision': 0.8738095238095238, 'recall': 0.8984088127294981, 'f1': 0.8859384429692213, 'number': 817} | {'precision': 0.5833333333333334, 'recall': 0.6470588235294118, 'f1': 0.6135458167330677, 'number': 119}   | {'precision': 0.9008341056533827, 'recall': 0.9025069637883009, 'f1': 0.901669758812616, 'number': 1077}  | 0.8693            | 0.8857         | 0.8775     | 0.8092           |
+| 0.0036        | 52.63  | 1000 | 1.6211          | {'precision': 0.8363228699551569, 'recall': 0.9130966952264382, 'f1': 0.8730251609128145, 'number': 817} | {'precision': 0.584070796460177, 'recall': 0.5546218487394958, 'f1': 0.5689655172413793, 'number': 119}    | {'precision': 0.8984302862419206, 'recall': 0.903435468895079, 'f1': 0.900925925925926, 'number': 1077}   | 0.8549            | 0.8867         | 0.8705     | 0.8039           |
+| 0.0029        | 63.16  | 1200 | 1.6274          | {'precision': 0.871007371007371, 'recall': 0.8678090575275398, 'f1': 0.8694052728387494, 'number': 817}  | {'precision': 0.5714285714285714, 'recall': 0.5042016806722689, 'f1': 0.5357142857142857, 'number': 119}   | {'precision': 0.8844404003639672, 'recall': 0.9025069637883009, 'f1': 0.8933823529411765, 'number': 1077} | 0.8627            | 0.8649         | 0.8638     | 0.8008           |
+| 0.0018        | 73.68  | 1400 | 1.6562          | {'precision': 0.8401360544217688, 'recall': 0.9069767441860465, 'f1': 0.872277810476751, 'number': 817}  | {'precision': 0.6132075471698113, 'recall': 0.5462184873949579, 'f1': 0.5777777777777778, 'number': 119}   | {'precision': 0.8892921960072595, 'recall': 0.9099350046425255, 'f1': 0.8994951812758146, 'number': 1077} | 0.8545            | 0.8872         | 0.8706     | 0.8096           |
+| 0.001         | 84.21  | 1600 | 1.6388          | {'precision': 0.8534090909090909, 'recall': 0.9192166462668299, 'f1': 0.8850913376546846, 'number': 817} | {'precision': 0.63, 'recall': 0.5294117647058824, 'f1': 0.5753424657534247, 'number': 119}                 | {'precision': 0.9009174311926605, 'recall': 0.9117920148560817, 'f1': 0.9063221042916475, 'number': 1077} | 0.8676            | 0.8922         | 0.8797     | 0.8103           |
+| 0.0007        | 94.74  | 1800 | 1.6278          | {'precision': 0.8545454545454545, 'recall': 0.9204406364749081, 'f1': 0.8862698880377136, 'number': 817} | {'precision': 0.6078431372549019, 'recall': 0.5210084033613446, 'f1': 0.5610859728506787, 'number': 119}   | {'precision': 0.8909740840035746, 'recall': 0.9257195914577531, 'f1': 0.9080145719489982, 'number': 1077} | 0.8620            | 0.8997         | 0.8804     | 0.8216           |
+| 0.0002        | 105.26 | 2000 | 1.6381          | {'precision': 0.8744075829383886, 'recall': 0.9033047735618115, 'f1': 0.8886213124623721, 'number': 817} | {'precision': 0.6261682242990654, 'recall': 0.5630252100840336, 'f1': 0.5929203539823009, 'number': 119}   | {'precision': 0.8998194945848376, 'recall': 0.9257195914577531, 'f1': 0.9125858123569794, 'number': 1077} | 0.8752            | 0.8952         | 0.8851     | 0.8174           |
+| 0.0002        | 115.79 | 2200 | 1.6545          | {'precision': 0.8757467144563919, 'recall': 0.8971848225214198, 'f1': 0.8863361547762998, 'number': 817} | {'precision': 0.625, 'recall': 0.5462184873949579, 'f1': 0.5829596412556054, 'number': 119}                | {'precision': 0.8902765388046388, 'recall': 0.9266480965645311, 'f1': 0.908098271155596, 'number': 1077}  | 0.8710            | 0.8922         | 0.8815     | 0.8155           |
+| 0.0002        | 126.32 | 2400 | 1.6477          | {'precision': 0.8658823529411764, 'recall': 0.9008567931456548, 'f1': 0.8830233953209357, 'number': 817} | {'precision': 0.6116504854368932, 'recall': 0.5294117647058824, 'f1': 0.5675675675675675, 'number': 119}   | {'precision': 0.8930817610062893, 'recall': 0.9229340761374187, 'f1': 0.9077625570776255, 'number': 1077} | 0.8679            | 0.8907         | 0.8791     | 0.8167           |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

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