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: 0.7403
+- Answer: {'precision': 0.73, 'recall': 0.8121137206427689, 'f1': 0.7688706846108836, 'number': 809}
+- Header: {'precision': 0.3611111111111111, 'recall': 0.4369747899159664, 'f1': 0.3954372623574144, 'number': 119}
+- Question: {'precision': 0.7853962600178095, 'recall': 0.828169014084507, 'f1': 0.8062157221206582, 'number': 1065}
+- Overall Precision: 0.7342
+- Overall Recall: 0.7983
+- Overall F1: 0.7649
+- Overall Accuracy: 0.8101
+
+## 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: 16
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                    | Header                                                                                                         | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.3197        | 1.0   | 10   | 1.0997          | {'precision': 0.34190231362467866, 'recall': 0.3288009888751545, 'f1': 0.3352236925015753, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.5646958011996572, 'recall': 0.6187793427230047, 'f1': 0.5905017921146953, 'number': 1065} | 0.4756            | 0.4641         | 0.4698     | 0.6432           |
+| 0.9556        | 2.0   | 20   | 0.8488          | {'precision': 0.5481481481481482, 'recall': 0.6402966625463535, 'f1': 0.5906499429874572, 'number': 809}  | {'precision': 0.038461538461538464, 'recall': 0.008403361344537815, 'f1': 0.013793103448275862, 'number': 119} | {'precision': 0.6639566395663956, 'recall': 0.6901408450704225, 'f1': 0.6767955801104972, 'number': 1065} | 0.6035            | 0.6292         | 0.6161     | 0.7343           |
+| 0.7263        | 3.0   | 30   | 0.7385          | {'precision': 0.645397489539749, 'recall': 0.7626699629171817, 'f1': 0.6991501416430596, 'number': 809}   | {'precision': 0.11320754716981132, 'recall': 0.05042016806722689, 'f1': 0.06976744186046512, 'number': 119}    | {'precision': 0.7092013888888888, 'recall': 0.7671361502347418, 'f1': 0.7370320252593595, 'number': 1065} | 0.6664            | 0.7225         | 0.6933     | 0.7743           |
+| 0.5842        | 4.0   | 40   | 0.6892          | {'precision': 0.6642487046632124, 'recall': 0.792336217552534, 'f1': 0.7226606538895153, 'number': 809}   | {'precision': 0.21686746987951808, 'recall': 0.15126050420168066, 'f1': 0.1782178217821782, 'number': 119}     | {'precision': 0.7226027397260274, 'recall': 0.7924882629107981, 'f1': 0.7559337214509628, 'number': 1065} | 0.6782            | 0.7541         | 0.7142     | 0.7964           |
+| 0.4945        | 5.0   | 50   | 0.6673          | {'precision': 0.6974416017797553, 'recall': 0.7750309023485785, 'f1': 0.734192037470726, 'number': 809}   | {'precision': 0.30337078651685395, 'recall': 0.226890756302521, 'f1': 0.2596153846153846, 'number': 119}       | {'precision': 0.7408637873754153, 'recall': 0.8375586854460094, 'f1': 0.7862494490965183, 'number': 1065} | 0.7053            | 0.7757         | 0.7388     | 0.8033           |
+| 0.4343        | 6.0   | 60   | 0.6592          | {'precision': 0.6962962962962963, 'recall': 0.8133498145859085, 'f1': 0.750285062713797, 'number': 809}   | {'precision': 0.29411764705882354, 'recall': 0.25210084033613445, 'f1': 0.27149321266968324, 'number': 119}    | {'precision': 0.7504173622704507, 'recall': 0.844131455399061, 'f1': 0.7945205479452054, 'number': 1065}  | 0.7069            | 0.7963         | 0.7489     | 0.8077           |
+| 0.3681        | 7.0   | 70   | 0.6624          | {'precision': 0.7049891540130152, 'recall': 0.8034610630407911, 'f1': 0.7510109763142693, 'number': 809}  | {'precision': 0.30158730158730157, 'recall': 0.31932773109243695, 'f1': 0.310204081632653, 'number': 119}      | {'precision': 0.7659758203799655, 'recall': 0.8328638497652582, 'f1': 0.7980206927575348, 'number': 1065} | 0.7140            | 0.7903         | 0.7502     | 0.8090           |
+| 0.3312        | 8.0   | 80   | 0.6825          | {'precision': 0.7097826086956521, 'recall': 0.8071693448702101, 'f1': 0.7553499132446501, 'number': 809}  | {'precision': 0.32142857142857145, 'recall': 0.37815126050420167, 'f1': 0.3474903474903475, 'number': 119}     | {'precision': 0.7703056768558952, 'recall': 0.828169014084507, 'f1': 0.7981900452488688, 'number': 1065}  | 0.7166            | 0.7928         | 0.7527     | 0.8078           |
+| 0.2955        | 9.0   | 90   | 0.7009          | {'precision': 0.7141316073354909, 'recall': 0.8182941903584673, 'f1': 0.7626728110599078, 'number': 809}  | {'precision': 0.3493150684931507, 'recall': 0.42857142857142855, 'f1': 0.38490566037735846, 'number': 119}     | {'precision': 0.7753108348134992, 'recall': 0.819718309859155, 'f1': 0.7968963943404839, 'number': 1065}  | 0.7212            | 0.7958         | 0.7567     | 0.8034           |
+| 0.2888        | 10.0  | 100  | 0.6894          | {'precision': 0.7125813449023861, 'recall': 0.8121137206427689, 'f1': 0.7590987868284228, 'number': 809}  | {'precision': 0.37272727272727274, 'recall': 0.3445378151260504, 'f1': 0.35807860262008734, 'number': 119}     | {'precision': 0.7917783735478106, 'recall': 0.831924882629108, 'f1': 0.8113553113553114, 'number': 1065}  | 0.7364            | 0.7948         | 0.7645     | 0.8140           |
+| 0.2482        | 11.0  | 110  | 0.7131          | {'precision': 0.7191854233654876, 'recall': 0.8294190358467244, 'f1': 0.7703788748564868, 'number': 809}  | {'precision': 0.3, 'recall': 0.40336134453781514, 'f1': 0.34408602150537637, 'number': 119}                    | {'precision': 0.7843833185448092, 'recall': 0.8300469483568075, 'f1': 0.8065693430656934, 'number': 1065} | 0.7221            | 0.8043         | 0.7610     | 0.8084           |
+| 0.2297        | 12.0  | 120  | 0.7189          | {'precision': 0.7373167981961668, 'recall': 0.8084054388133498, 'f1': 0.7712264150943396, 'number': 809}  | {'precision': 0.3484848484848485, 'recall': 0.3865546218487395, 'f1': 0.3665338645418326, 'number': 119}       | {'precision': 0.7730434782608696, 'recall': 0.8347417840375587, 'f1': 0.8027088036117382, 'number': 1065} | 0.7326            | 0.7973         | 0.7636     | 0.8125           |
+| 0.2168        | 13.0  | 130  | 0.7283          | {'precision': 0.723986856516977, 'recall': 0.8170580964153276, 'f1': 0.7677119628339142, 'number': 809}   | {'precision': 0.33793103448275863, 'recall': 0.4117647058823529, 'f1': 0.37121212121212116, 'number': 119}     | {'precision': 0.7878245299910475, 'recall': 0.8262910798122066, 'f1': 0.8065994500458296, 'number': 1065} | 0.7310            | 0.7978         | 0.7630     | 0.8099           |
+| 0.2011        | 14.0  | 140  | 0.7318          | {'precision': 0.7338530066815144, 'recall': 0.8145859085290482, 'f1': 0.7721148213239603, 'number': 809}  | {'precision': 0.3493150684931507, 'recall': 0.42857142857142855, 'f1': 0.38490566037735846, 'number': 119}     | {'precision': 0.7833775419982316, 'recall': 0.831924882629108, 'f1': 0.8069216757741348, 'number': 1065}  | 0.7338            | 0.8008         | 0.7658     | 0.8112           |
+| 0.1948        | 15.0  | 150  | 0.7391          | {'precision': 0.7216721672167217, 'recall': 0.8108776266996292, 'f1': 0.7636786961583235, 'number': 809}  | {'precision': 0.3561643835616438, 'recall': 0.4369747899159664, 'f1': 0.39245283018867927, 'number': 119}      | {'precision': 0.7848214285714286, 'recall': 0.8253521126760563, 'f1': 0.8045766590389016, 'number': 1065} | 0.7297            | 0.7963         | 0.7615     | 0.8076           |
+| 0.1955        | 16.0  | 160  | 0.7403          | {'precision': 0.73, 'recall': 0.8121137206427689, 'f1': 0.7688706846108836, 'number': 809}                | {'precision': 0.3611111111111111, 'recall': 0.4369747899159664, 'f1': 0.3954372623574144, 'number': 119}       | {'precision': 0.7853962600178095, 'recall': 0.828169014084507, 'f1': 0.8062157221206582, 'number': 1065}  | 0.7342            | 0.7983         | 0.7649     | 0.8101           |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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preprocessor_config.json

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special_tokens_map.json

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tokenizer_config.json

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