End of training

README.md

@@ -0,0 +1,81 @@
+---
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutlm-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. -->
+
+# layoutlm-funsd
+
+This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6739
+- Answer: {'precision': 0.7077087794432548, 'recall': 0.8170580964153276, 'f1': 0.7584624211130234, 'number': 809}
+- Header: {'precision': 0.30656934306569344, 'recall': 0.35294117647058826, 'f1': 0.32812500000000006, 'number': 119}
+- Question: {'precision': 0.7837354781054513, 'recall': 0.8234741784037559, 'f1': 0.8031135531135531, 'number': 1065}
+- Overall Precision: 0.7215
+- Overall Recall: 0.7928
+- Overall F1: 0.7554
+- Overall Accuracy: 0.8075
+
+## 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: 16
+- eval_batch_size: 8
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7578        | 1.0   | 10   | 1.5659          | {'precision': 0.020053475935828877, 'recall': 0.018541409147095178, 'f1': 0.01926782273603083, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.311886586695747, 'recall': 0.26854460093896715, 'f1': 0.2885973763874874, 'number': 1065}  | 0.1808            | 0.1510         | 0.1646     | 0.3760           |
+| 1.409         | 2.0   | 20   | 1.2205          | {'precision': 0.220795892169448, 'recall': 0.2126081582200247, 'f1': 0.21662468513853905, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.43257184966838613, 'recall': 0.5511737089201878, 'f1': 0.4847233691164327, 'number': 1065} | 0.3553            | 0.3808         | 0.3676     | 0.5932           |
+| 1.0728        | 3.0   | 30   | 0.9396          | {'precision': 0.5072765072765073, 'recall': 0.6032138442521632, 'f1': 0.5511010728402033, 'number': 809}      | {'precision': 0.02702702702702703, 'recall': 0.008403361344537815, 'f1': 0.01282051282051282, 'number': 119} | {'precision': 0.5947242206235012, 'recall': 0.6985915492957746, 'f1': 0.6424870466321244, 'number': 1065}  | 0.548             | 0.6187         | 0.5812     | 0.7236           |
+| 0.8188        | 4.0   | 40   | 0.7725          | {'precision': 0.6076845298281092, 'recall': 0.7428924598269468, 'f1': 0.6685205784204672, 'number': 809}      | {'precision': 0.18, 'recall': 0.07563025210084033, 'f1': 0.10650887573964496, 'number': 119}                 | {'precision': 0.6797608881298036, 'recall': 0.7474178403755869, 'f1': 0.7119856887298748, 'number': 1065}  | 0.6362            | 0.7055         | 0.6690     | 0.7680           |
+| 0.6647        | 5.0   | 50   | 0.7205          | {'precision': 0.6301806588735388, 'recall': 0.7330037082818294, 'f1': 0.6777142857142857, 'number': 809}      | {'precision': 0.22093023255813954, 'recall': 0.15966386554621848, 'f1': 0.18536585365853656, 'number': 119}  | {'precision': 0.6648731744811683, 'recall': 0.812206572769953, 'f1': 0.7311918850380389, 'number': 1065}   | 0.6345            | 0.7411         | 0.6836     | 0.7775           |
+| 0.5719        | 6.0   | 60   | 0.6793          | {'precision': 0.6366336633663366, 'recall': 0.7948084054388134, 'f1': 0.7069818581638262, 'number': 809}      | {'precision': 0.25301204819277107, 'recall': 0.17647058823529413, 'f1': 0.20792079207920794, 'number': 119}  | {'precision': 0.7342342342342343, 'recall': 0.7652582159624414, 'f1': 0.749425287356322, 'number': 1065}   | 0.6714            | 0.7421         | 0.7050     | 0.7826           |
+| 0.5011        | 7.0   | 70   | 0.6617          | {'precision': 0.6697819314641744, 'recall': 0.7972805933250927, 'f1': 0.7279909706546276, 'number': 809}      | {'precision': 0.24347826086956523, 'recall': 0.23529411764705882, 'f1': 0.23931623931623933, 'number': 119}  | {'precision': 0.7497773820124666, 'recall': 0.7906103286384977, 'f1': 0.7696526508226691, 'number': 1065}  | 0.6883            | 0.7602         | 0.7225     | 0.7929           |
+| 0.4478        | 8.0   | 80   | 0.6529          | {'precision': 0.6725755995828988, 'recall': 0.7972805933250927, 'f1': 0.7296380090497737, 'number': 809}      | {'precision': 0.23577235772357724, 'recall': 0.24369747899159663, 'f1': 0.23966942148760334, 'number': 119}  | {'precision': 0.7578397212543554, 'recall': 0.8169014084507042, 'f1': 0.7862629914143697, 'number': 1065}  | 0.6924            | 0.7747         | 0.7312     | 0.8001           |
+| 0.3901        | 9.0   | 90   | 0.6513          | {'precision': 0.6936353829557713, 'recall': 0.7948084054388134, 'f1': 0.7407834101382489, 'number': 809}      | {'precision': 0.27906976744186046, 'recall': 0.3025210084033613, 'f1': 0.29032258064516125, 'number': 119}   | {'precision': 0.7517123287671232, 'recall': 0.8244131455399061, 'f1': 0.7863860277653381, 'number': 1065}  | 0.7001            | 0.7812         | 0.7384     | 0.8034           |
+| 0.3881        | 10.0  | 100  | 0.6564          | {'precision': 0.685890834191555, 'recall': 0.823238566131026, 'f1': 0.7483146067415729, 'number': 809}        | {'precision': 0.3063063063063063, 'recall': 0.2857142857142857, 'f1': 0.2956521739130435, 'number': 119}     | {'precision': 0.7702582368655387, 'recall': 0.812206572769953, 'f1': 0.7906764168190127, 'number': 1065}   | 0.7098            | 0.7852         | 0.7456     | 0.8075           |
+| 0.3249        | 11.0  | 110  | 0.6580          | {'precision': 0.7036247334754797, 'recall': 0.8158220024721878, 'f1': 0.755580995993131, 'number': 809}       | {'precision': 0.31007751937984496, 'recall': 0.33613445378151263, 'f1': 0.3225806451612903, 'number': 119}   | {'precision': 0.7693646649260226, 'recall': 0.8300469483568075, 'f1': 0.7985546522131888, 'number': 1065}  | 0.7148            | 0.7948         | 0.7527     | 0.8088           |
+| 0.3099        | 12.0  | 120  | 0.6646          | {'precision': 0.7090909090909091, 'recall': 0.8195302843016069, 'f1': 0.7603211009174312, 'number': 809}      | {'precision': 0.29411764705882354, 'recall': 0.33613445378151263, 'f1': 0.3137254901960785, 'number': 119}   | {'precision': 0.7797672336615935, 'recall': 0.8178403755868544, 'f1': 0.7983501374885427, 'number': 1065}  | 0.7194            | 0.7898         | 0.7529     | 0.8098           |
+| 0.2907        | 13.0  | 130  | 0.6653          | {'precision': 0.7141316073354909, 'recall': 0.8182941903584673, 'f1': 0.7626728110599078, 'number': 809}      | {'precision': 0.3125, 'recall': 0.33613445378151263, 'f1': 0.3238866396761134, 'number': 119}                | {'precision': 0.7902790279027903, 'recall': 0.8244131455399061, 'f1': 0.806985294117647, 'number': 1065}   | 0.7295            | 0.7928         | 0.7598     | 0.8104           |
+| 0.2715        | 14.0  | 140  | 0.6720          | {'precision': 0.71259418729817, 'recall': 0.8182941903584673, 'f1': 0.761795166858458, 'number': 809}         | {'precision': 0.31343283582089554, 'recall': 0.35294117647058826, 'f1': 0.3320158102766798, 'number': 119}   | {'precision': 0.7867383512544803, 'recall': 0.8244131455399061, 'f1': 0.8051352590554791, 'number': 1065}  | 0.7260            | 0.7938         | 0.7584     | 0.8078           |
+| 0.2743        | 15.0  | 150  | 0.6739          | {'precision': 0.7077087794432548, 'recall': 0.8170580964153276, 'f1': 0.7584624211130234, 'number': 809}      | {'precision': 0.30656934306569344, 'recall': 0.35294117647058826, 'f1': 0.32812500000000006, 'number': 119}  | {'precision': 0.7837354781054513, 'recall': 0.8234741784037559, 'f1': 0.8031135531135531, 'number': 1065}  | 0.7215            | 0.7928         | 0.7554     | 0.8075           |
+
+
+### Framework versions
+
+- Transformers 4.40.0
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.0
+- Tokenizers 0.19.1

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preprocessor_config.json

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+  "_valid_processor_keys": [
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special_tokens_map.json

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tokenizer_config.json

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+}