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-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: 1.6046
+- Answer: {'precision': 0.8879518072289156, 'recall': 0.9020807833537332, 'f1': 0.8949605343047966, 'number': 817}
+- Header: {'precision': 0.6761904761904762, 'recall': 0.5966386554621849, 'f1': 0.6339285714285715, 'number': 119}
+- Question: {'precision': 0.8968609865470852, 'recall': 0.9285051067780873, 'f1': 0.9124087591240877, 'number': 1077}
+- Overall Precision: 0.8820
+- Overall Recall: 0.8982
+- Overall F1: 0.8900
+- Overall Accuracy: 0.8187
+
+## 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 |
+|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.3984        | 10.5263  | 200  | 0.9319          | {'precision': 0.837129840546697, 'recall': 0.8996328029375765, 'f1': 0.8672566371681416, 'number': 817}  | {'precision': 0.5238095238095238, 'recall': 0.46218487394957986, 'f1': 0.4910714285714286, 'number': 119} | {'precision': 0.8606060606060606, 'recall': 0.9229340761374187, 'f1': 0.8906810035842293, 'number': 1077} | 0.8344            | 0.8862         | 0.8596     | 0.7926           |
+| 0.0543        | 21.0526  | 400  | 1.1018          | {'precision': 0.8492520138089759, 'recall': 0.9033047735618115, 'f1': 0.8754448398576511, 'number': 817} | {'precision': 0.5688073394495413, 'recall': 0.5210084033613446, 'f1': 0.543859649122807, 'number': 119}   | {'precision': 0.8731277533039647, 'recall': 0.9201485608170845, 'f1': 0.8960216998191681, 'number': 1077} | 0.8476            | 0.8897         | 0.8682     | 0.8133           |
+| 0.0134        | 31.5789  | 600  | 1.5231          | {'precision': 0.838963963963964, 'recall': 0.9118727050183598, 'f1': 0.873900293255132, 'number': 817}   | {'precision': 0.5727272727272728, 'recall': 0.5294117647058824, 'f1': 0.5502183406113538, 'number': 119}  | {'precision': 0.9149338374291115, 'recall': 0.8987929433611885, 'f1': 0.9067915690866512, 'number': 1077} | 0.8638            | 0.8823         | 0.8729     | 0.8046           |
+| 0.0101        | 42.1053  | 800  | 1.5678          | {'precision': 0.8509895227008148, 'recall': 0.8947368421052632, 'f1': 0.8723150357995225, 'number': 817} | {'precision': 0.5700934579439252, 'recall': 0.5126050420168067, 'f1': 0.5398230088495575, 'number': 119}  | {'precision': 0.8781770376862401, 'recall': 0.9303621169916435, 'f1': 0.9035166816952209, 'number': 1077} | 0.8514            | 0.8912         | 0.8709     | 0.8052           |
+| 0.0041        | 52.6316  | 1000 | 1.6538          | {'precision': 0.8399558498896247, 'recall': 0.9314565483476133, 'f1': 0.8833430063842136, 'number': 817} | {'precision': 0.6705882352941176, 'recall': 0.4789915966386555, 'f1': 0.5588235294117647, 'number': 119}  | {'precision': 0.9063386944181646, 'recall': 0.8895078922934077, 'f1': 0.8978444236176195, 'number': 1077} | 0.8672            | 0.8823         | 0.8747     | 0.7979           |
+| 0.0033        | 63.1579  | 1200 | 1.4464          | {'precision': 0.875, 'recall': 0.9167686658506732, 'f1': 0.895397489539749, 'number': 817}               | {'precision': 0.6049382716049383, 'recall': 0.4117647058823529, 'f1': 0.49000000000000005, 'number': 119} | {'precision': 0.8777292576419214, 'recall': 0.9331476323119777, 'f1': 0.9045904590459046, 'number': 1077} | 0.8660            | 0.8957         | 0.8806     | 0.8152           |
+| 0.0015        | 73.6842  | 1400 | 1.5128          | {'precision': 0.8679906542056075, 'recall': 0.9094247246022031, 'f1': 0.8882247459653317, 'number': 817} | {'precision': 0.6511627906976745, 'recall': 0.47058823529411764, 'f1': 0.5463414634146342, 'number': 119} | {'precision': 0.8906810035842294, 'recall': 0.9229340761374187, 'f1': 0.9065207478340173, 'number': 1077} | 0.8712            | 0.8907         | 0.8809     | 0.8213           |
+| 0.0014        | 84.2105  | 1600 | 1.6089          | {'precision': 0.8555176336746303, 'recall': 0.9204406364749081, 'f1': 0.8867924528301887, 'number': 817} | {'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119}   | {'precision': 0.8891820580474934, 'recall': 0.9387186629526463, 'f1': 0.913279132791328, 'number': 1077}  | 0.8610            | 0.9081         | 0.8839     | 0.8172           |
+| 0.0005        | 94.7368  | 1800 | 1.6500          | {'precision': 0.865967365967366, 'recall': 0.9094247246022031, 'f1': 0.8871641791044775, 'number': 817}  | {'precision': 0.6513761467889908, 'recall': 0.5966386554621849, 'f1': 0.6228070175438596, 'number': 119}  | {'precision': 0.9027522935779817, 'recall': 0.9136490250696379, 'f1': 0.9081679741578219, 'number': 1077} | 0.8741            | 0.8932         | 0.8835     | 0.8135           |
+| 0.0005        | 105.2632 | 2000 | 1.6204          | {'precision': 0.8909090909090909, 'recall': 0.8996328029375765, 'f1': 0.8952496954933008, 'number': 817} | {'precision': 0.6403508771929824, 'recall': 0.6134453781512605, 'f1': 0.6266094420600858, 'number': 119}  | {'precision': 0.8925399644760214, 'recall': 0.9331476323119777, 'f1': 0.9123921924648207, 'number': 1077} | 0.8780            | 0.9006         | 0.8892     | 0.8177           |
+| 0.0004        | 115.7895 | 2200 | 1.6046          | {'precision': 0.8879518072289156, 'recall': 0.9020807833537332, 'f1': 0.8949605343047966, 'number': 817} | {'precision': 0.6761904761904762, 'recall': 0.5966386554621849, 'f1': 0.6339285714285715, 'number': 119}  | {'precision': 0.8968609865470852, 'recall': 0.9285051067780873, 'f1': 0.9124087591240877, 'number': 1077} | 0.8820            | 0.8982         | 0.8900     | 0.8187           |
+| 0.0002        | 126.3158 | 2400 | 1.6270          | {'precision': 0.8790035587188612, 'recall': 0.9069767441860465, 'f1': 0.8927710843373493, 'number': 817} | {'precision': 0.6574074074074074, 'recall': 0.5966386554621849, 'f1': 0.6255506607929515, 'number': 119}  | {'precision': 0.8972046889089269, 'recall': 0.9238625812441968, 'f1': 0.9103385178408051, 'number': 1077} | 0.8772            | 0.8977         | 0.8873     | 0.8193           |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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