layoutlm-funsd2
This model is a fine-tuned version of srinidhireddy1604/layoutlm-funsd on the funsd dataset. It achieves the following results on the evaluation set:
- Loss: 0.8155
- Answer: {'precision': 0.7465437788018433, 'recall': 0.8009888751545118, 'f1': 0.7728085867620751, 'number': 809}
- Header: {'precision': 0.3597122302158273, 'recall': 0.42016806722689076, 'f1': 0.38759689922480617, 'number': 119}
- Question: {'precision': 0.8012533572068039, 'recall': 0.8403755868544601, 'f1': 0.8203483043079742, 'number': 1065}
- Overall Precision: 0.75
- Overall Recall: 0.7993
- Overall F1: 0.7739
- Overall Accuracy: 0.8050
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.5269 | 1.0 | 5 | 0.7058 | {'precision': 0.6515151515151515, 'recall': 0.7972805933250927, 'f1': 0.717065036131184, 'number': 809} | {'precision': 0.4032258064516129, 'recall': 0.21008403361344538, 'f1': 0.2762430939226519, 'number': 119} | {'precision': 0.7341659232827832, 'recall': 0.7727699530516432, 'f1': 0.7529734675205856, 'number': 1065} | 0.6871 | 0.7491 | 0.7168 | 0.7949 |
| 0.4603 | 2.0 | 10 | 0.7202 | {'precision': 0.6969339622641509, 'recall': 0.73053152039555, 'f1': 0.7133373566686784, 'number': 809} | {'precision': 0.2896551724137931, 'recall': 0.35294117647058826, 'f1': 0.31818181818181823, 'number': 119} | {'precision': 0.7457777777777778, 'recall': 0.787793427230047, 'f1': 0.7662100456621005, 'number': 1065} | 0.6950 | 0.7386 | 0.7161 | 0.7921 |
| 0.4097 | 3.0 | 15 | 0.6983 | {'precision': 0.7, 'recall': 0.7787391841779975, 'f1': 0.7372732592159158, 'number': 809} | {'precision': 0.30701754385964913, 'recall': 0.29411764705882354, 'f1': 0.30042918454935624, 'number': 119} | {'precision': 0.7397831526271893, 'recall': 0.8328638497652582, 'f1': 0.7835689045936396, 'number': 1065} | 0.7013 | 0.7787 | 0.7380 | 0.8069 |
| 0.3584 | 4.0 | 20 | 0.7083 | {'precision': 0.7253121452894438, 'recall': 0.7898640296662547, 'f1': 0.7562130177514793, 'number': 809} | {'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119} | {'precision': 0.7732610659439928, 'recall': 0.8037558685446009, 'f1': 0.7882136279926335, 'number': 1065} | 0.7278 | 0.7702 | 0.7484 | 0.8095 |
| 0.325 | 5.0 | 25 | 0.7201 | {'precision': 0.6947483588621444, 'recall': 0.7849196538936959, 'f1': 0.7370864770748694, 'number': 809} | {'precision': 0.2962962962962963, 'recall': 0.33613445378151263, 'f1': 0.31496062992125984, 'number': 119} | {'precision': 0.7645021645021645, 'recall': 0.8291079812206573, 'f1': 0.7954954954954955, 'number': 1065} | 0.7069 | 0.7817 | 0.7424 | 0.8031 |
| 0.292 | 6.0 | 30 | 0.7411 | {'precision': 0.7247706422018348, 'recall': 0.7812113720642769, 'f1': 0.7519333729922665, 'number': 809} | {'precision': 0.36036036036036034, 'recall': 0.33613445378151263, 'f1': 0.34782608695652173, 'number': 119} | {'precision': 0.7846846846846847, 'recall': 0.8178403755868544, 'f1': 0.800919540229885, 'number': 1065} | 0.7372 | 0.7742 | 0.7553 | 0.8057 |
| 0.2752 | 7.0 | 35 | 0.7645 | {'precision': 0.7170022371364653, 'recall': 0.792336217552534, 'f1': 0.7527891955372871, 'number': 809} | {'precision': 0.3032258064516129, 'recall': 0.3949579831932773, 'f1': 0.34306569343065696, 'number': 119} | {'precision': 0.7769848349687779, 'recall': 0.8178403755868544, 'f1': 0.7968892955169259, 'number': 1065} | 0.7184 | 0.7822 | 0.7490 | 0.7943 |
| 0.2508 | 8.0 | 40 | 0.7613 | {'precision': 0.7331081081081081, 'recall': 0.8046971569839307, 'f1': 0.7672362993517973, 'number': 809} | {'precision': 0.34959349593495936, 'recall': 0.36134453781512604, 'f1': 0.35537190082644626, 'number': 119} | {'precision': 0.7905944986690329, 'recall': 0.8366197183098592, 'f1': 0.8129562043795621, 'number': 1065} | 0.7413 | 0.7953 | 0.7674 | 0.8012 |
| 0.2305 | 9.0 | 45 | 0.7761 | {'precision': 0.7379862700228833, 'recall': 0.7972805933250927, 'f1': 0.7664884135472371, 'number': 809} | {'precision': 0.3161290322580645, 'recall': 0.4117647058823529, 'f1': 0.35766423357664234, 'number': 119} | {'precision': 0.7846975088967971, 'recall': 0.828169014084507, 'f1': 0.8058474189127455, 'number': 1065} | 0.7320 | 0.7908 | 0.7603 | 0.8018 |
| 0.2201 | 10.0 | 50 | 0.7905 | {'precision': 0.7369614512471655, 'recall': 0.8034610630407911, 'f1': 0.768775872264932, 'number': 809} | {'precision': 0.3409090909090909, 'recall': 0.37815126050420167, 'f1': 0.3585657370517928, 'number': 119} | {'precision': 0.791814946619217, 'recall': 0.8356807511737089, 'f1': 0.8131566925536775, 'number': 1065} | 0.7413 | 0.7953 | 0.7674 | 0.8033 |
| 0.2091 | 11.0 | 55 | 0.8025 | {'precision': 0.7281879194630873, 'recall': 0.8046971569839307, 'f1': 0.7645331767469172, 'number': 809} | {'precision': 0.33783783783783783, 'recall': 0.42016806722689076, 'f1': 0.37453183520599254, 'number': 119} | {'precision': 0.8009009009009009, 'recall': 0.8347417840375587, 'f1': 0.8174712643678161, 'number': 1065} | 0.7388 | 0.7978 | 0.7672 | 0.8014 |
| 0.197 | 12.0 | 60 | 0.8051 | {'precision': 0.74230330672748, 'recall': 0.8046971569839307, 'f1': 0.7722419928825623, 'number': 809} | {'precision': 0.3401360544217687, 'recall': 0.42016806722689076, 'f1': 0.37593984962406013, 'number': 119} | {'precision': 0.8025247971145176, 'recall': 0.8356807511737089, 'f1': 0.8187672493100275, 'number': 1065} | 0.7459 | 0.7983 | 0.7712 | 0.8035 |
| 0.1899 | 13.0 | 65 | 0.8079 | {'precision': 0.7448275862068966, 'recall': 0.8009888751545118, 'f1': 0.7718880285884455, 'number': 809} | {'precision': 0.3671875, 'recall': 0.3949579831932773, 'f1': 0.3805668016194332, 'number': 119} | {'precision': 0.7996422182468694, 'recall': 0.8394366197183099, 'f1': 0.8190563444800733, 'number': 1065} | 0.7509 | 0.7973 | 0.7734 | 0.8048 |
| 0.1839 | 14.0 | 70 | 0.8127 | {'precision': 0.7442660550458715, 'recall': 0.8022249690976514, 'f1': 0.7721594289113624, 'number': 809} | {'precision': 0.36496350364963503, 'recall': 0.42016806722689076, 'f1': 0.390625, 'number': 119} | {'precision': 0.8019713261648745, 'recall': 0.8403755868544601, 'f1': 0.8207244383310408, 'number': 1065} | 0.7501 | 0.7998 | 0.7742 | 0.8056 |
| 0.1821 | 15.0 | 75 | 0.8155 | {'precision': 0.7465437788018433, 'recall': 0.8009888751545118, 'f1': 0.7728085867620751, 'number': 809} | {'precision': 0.3597122302158273, 'recall': 0.42016806722689076, 'f1': 0.38759689922480617, 'number': 119} | {'precision': 0.8012533572068039, 'recall': 0.8403755868544601, 'f1': 0.8203483043079742, 'number': 1065} | 0.75 | 0.7993 | 0.7739 | 0.8050 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0