End of training

README.md

@@ -16,19 +16,15 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [philschmid/lilt-en-funsd](https://huggingface.co/philschmid/lilt-en-funsd) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- eval_loss: 1.6118
-- eval_model_preparation_time: 0.0209
-- eval_ANSWER: {'precision': 0.8821428571428571, 'recall': 0.9069767441860465, 'f1': 0.8943874471937237, 'number': 817}
-- eval_HEADER: {'precision': 0.6126126126126126, 'recall': 0.5714285714285714, 'f1': 0.591304347826087, 'number': 119}
-- eval_QUESTION: {'precision': 0.9045045045045045, 'recall': 0.9322191272051996, 'f1': 0.9181527206218564, 'number': 1077}
-- eval_overall_precision: 0.8797
-- eval_overall_recall: 0.9006
-- eval_overall_f1: 0.8900
-- eval_overall_accuracy: 0.8204
-- eval_runtime: 2.1591
-- eval_samples_per_second: 23.158
-- eval_steps_per_second: 3.242
-- step: 0
+- Loss: 1.6942
+- Model Preparation Time: 0.0209
+- Answer: {'precision': 0.8819951338199513, 'recall': 0.8873929008567931, 'f1': 0.8846857840146432, 'number': 817}
+- Header: {'precision': 0.6068376068376068, 'recall': 0.5966386554621849, 'f1': 0.6016949152542374, 'number': 119}
+- Question: {'precision': 0.8883928571428571, 'recall': 0.9238625812441968, 'f1': 0.9057806099226217, 'number': 1077}
+- Overall Precision: 0.8698
+- Overall Recall: 0.8897
+- Overall F1: 0.8797
+- Overall Accuracy: 0.8087
 
 ## Model description
 
@@ -56,6 +52,24 @@ The following hyperparameters were used during training:
 - training_steps: 2500
 - mixed_precision_training: Native AMP
 
+### Training results
+
+| Training Loss | Epoch    | Step | Validation Loss | Model Preparation Time | Answer                                                                                                   | Header                                                                                                   | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:--------:|:----:|:---------------:|:----------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0084        | 10.5263  | 200  | 2.0754          | 0.0209                 | {'precision': 0.8609355246523388, 'recall': 0.8335373317013464, 'f1': 0.8470149253731344, 'number': 817} | {'precision': 0.6262626262626263, 'recall': 0.5210084033613446, 'f1': 0.5688073394495413, 'number': 119} | {'precision': 0.8900675024108003, 'recall': 0.8570102135561746, 'f1': 0.8732261116367076, 'number': 1077} | 0.8646            | 0.8276         | 0.8457     | 0.7701           |
+| 0.0071        | 21.0526  | 400  | 1.8309          | 0.0209                 | {'precision': 0.8639125151883353, 'recall': 0.8702570379436965, 'f1': 0.8670731707317074, 'number': 817} | {'precision': 0.5294117647058824, 'recall': 0.6050420168067226, 'f1': 0.5647058823529412, 'number': 119} | {'precision': 0.8911819887429644, 'recall': 0.8820798514391829, 'f1': 0.8866075594960336, 'number': 1077} | 0.8558            | 0.8609         | 0.8583     | 0.7862           |
+| 0.0042        | 31.5789  | 600  | 1.6681          | 0.0209                 | {'precision': 0.8744019138755981, 'recall': 0.8947368421052632, 'f1': 0.8844525105868118, 'number': 817} | {'precision': 0.6101694915254238, 'recall': 0.6050420168067226, 'f1': 0.6075949367088608, 'number': 119} | {'precision': 0.8810810810810811, 'recall': 0.9080779944289693, 'f1': 0.8943758573388202, 'number': 1077} | 0.8629            | 0.8847         | 0.8737     | 0.7992           |
+| 0.0031        | 42.1053  | 800  | 1.8385          | 0.0209                 | {'precision': 0.8792270531400966, 'recall': 0.8910648714810282, 'f1': 0.8851063829787235, 'number': 817} | {'precision': 0.6355140186915887, 'recall': 0.5714285714285714, 'f1': 0.6017699115044248, 'number': 119} | {'precision': 0.8826666666666667, 'recall': 0.9220055710306406, 'f1': 0.9019073569482289, 'number': 1077} | 0.8684            | 0.8887         | 0.8785     | 0.7985           |
+| 0.0024        | 52.6316  | 1000 | 1.9272          | 0.0209                 | {'precision': 0.8584579976985041, 'recall': 0.9130966952264382, 'f1': 0.8849347568208777, 'number': 817} | {'precision': 0.6, 'recall': 0.5798319327731093, 'f1': 0.5897435897435898, 'number': 119}                | {'precision': 0.9062801932367149, 'recall': 0.8709377901578459, 'f1': 0.8882575757575758, 'number': 1077} | 0.8683            | 0.8708         | 0.8695     | 0.7878           |
+| 0.0014        | 63.1579  | 1200 | 1.6942          | 0.0209                 | {'precision': 0.8819951338199513, 'recall': 0.8873929008567931, 'f1': 0.8846857840146432, 'number': 817} | {'precision': 0.6068376068376068, 'recall': 0.5966386554621849, 'f1': 0.6016949152542374, 'number': 119} | {'precision': 0.8883928571428571, 'recall': 0.9238625812441968, 'f1': 0.9057806099226217, 'number': 1077} | 0.8698            | 0.8897         | 0.8797     | 0.8087           |
+| 0.0011        | 73.6842  | 1400 | 1.7932          | 0.0209                 | {'precision': 0.8366445916114791, 'recall': 0.9277845777233782, 'f1': 0.8798607080673244, 'number': 817} | {'precision': 0.673469387755102, 'recall': 0.5546218487394958, 'f1': 0.6082949308755761, 'number': 119}  | {'precision': 0.9147727272727273, 'recall': 0.8969359331476323, 'f1': 0.9057665260196907, 'number': 1077} | 0.8689            | 0.8892         | 0.8790     | 0.8038           |
+| 0.0005        | 84.2105  | 1600 | 1.7226          | 0.0209                 | {'precision': 0.8409610983981693, 'recall': 0.8996328029375765, 'f1': 0.8693081017149615, 'number': 817} | {'precision': 0.6228070175438597, 'recall': 0.5966386554621849, 'f1': 0.6094420600858369, 'number': 119} | {'precision': 0.910377358490566, 'recall': 0.8960074280408542, 'f1': 0.9031352363125876, 'number': 1077}  | 0.8647            | 0.8798         | 0.8722     | 0.8063           |
+| 0.0002        | 94.7368  | 1800 | 1.7975          | 0.0209                 | {'precision': 0.8627450980392157, 'recall': 0.9155446756425949, 'f1': 0.8883610451306414, 'number': 817} | {'precision': 0.5655737704918032, 'recall': 0.5798319327731093, 'f1': 0.5726141078838175, 'number': 119} | {'precision': 0.9010082493125573, 'recall': 0.9127205199628597, 'f1': 0.9068265682656828, 'number': 1077} | 0.8654            | 0.8942         | 0.8796     | 0.8064           |
+| 0.0001        | 105.2632 | 2000 | 1.8298          | 0.0209                 | {'precision': 0.8683901292596945, 'recall': 0.9045287637698899, 'f1': 0.8860911270983215, 'number': 817} | {'precision': 0.5737704918032787, 'recall': 0.5882352941176471, 'f1': 0.5809128630705394, 'number': 119} | {'precision': 0.9000916590284143, 'recall': 0.9117920148560817, 'f1': 0.9059040590405903, 'number': 1077} | 0.8677            | 0.8897         | 0.8786     | 0.8060           |
+| 0.0002        | 115.7895 | 2200 | 1.8057          | 0.0209                 | {'precision': 0.8759036144578313, 'recall': 0.8898408812729498, 'f1': 0.8828172434729811, 'number': 817} | {'precision': 0.6016949152542372, 'recall': 0.5966386554621849, 'f1': 0.5991561181434599, 'number': 119} | {'precision': 0.896551724137931, 'recall': 0.9173630454967502, 'f1': 0.9068379990821477, 'number': 1077}  | 0.8712            | 0.8872         | 0.8792     | 0.8095           |
+| 0.0001        | 126.3158 | 2400 | 1.8181          | 0.0209                 | {'precision': 0.8708133971291866, 'recall': 0.8910648714810282, 'f1': 0.8808227465214761, 'number': 817} | {'precision': 0.5867768595041323, 'recall': 0.5966386554621849, 'f1': 0.5916666666666667, 'number': 119} | {'precision': 0.894927536231884, 'recall': 0.9173630454967502, 'f1': 0.9060064190738194, 'number': 1077}  | 0.8671            | 0.8877         | 0.8773     | 0.8061           |
+
+
 ### Framework versions
 
 - Transformers 4.51.3

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