End of training

README.md

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+---
+library_name: transformers
+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: 0.7033
+- Answer: {'precision': 0.7135016465422612, 'recall': 0.8034610630407911, 'f1': 0.7558139534883722, 'number': 809}
+- Header: {'precision': 0.34959349593495936, 'recall': 0.36134453781512604, 'f1': 0.35537190082644626, 'number': 119}
+- Question: {'precision': 0.7895204262877442, 'recall': 0.8347417840375587, 'f1': 0.8115015974440895, 'number': 1065}
+- Overall Precision: 0.7324
+- Overall Recall: 0.7938
+- Overall F1: 0.7619
+- Overall Accuracy: 0.8096
+
+## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7852        | 1.0   | 10   | 1.6133          | {'precision': 0.0075, 'recall': 0.003708281829419036, 'f1': 0.004962779156327543, 'number': 809}          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.27348066298342544, 'recall': 0.09295774647887324, 'f1': 0.13875262789067977, 'number': 1065} | 0.1339            | 0.0512         | 0.0740     | 0.3219           |
+| 1.4748        | 2.0   | 20   | 1.2897          | {'precision': 0.13947696139476962, 'recall': 0.138442521631644, 'f1': 0.13895781637717122, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.4375, 'recall': 0.5126760563380282, 'f1': 0.4721141374837873, 'number': 1065}                | 0.3208            | 0.3302         | 0.3254     | 0.5949           |
+| 1.1297        | 3.0   | 30   | 0.9650          | {'precision': 0.4776931447225245, 'recall': 0.5426452410383189, 'f1': 0.5081018518518519, 'number': 809}  | {'precision': 0.14285714285714285, 'recall': 0.025210084033613446, 'f1': 0.04285714285714286, 'number': 119} | {'precision': 0.5609561752988048, 'recall': 0.6610328638497652, 'f1': 0.606896551724138, 'number': 1065}     | 0.5221            | 0.5750         | 0.5473     | 0.7046           |
+| 0.8686        | 4.0   | 40   | 0.7908          | {'precision': 0.6090534979423868, 'recall': 0.7317676143386898, 'f1': 0.664795058955643, 'number': 809}   | {'precision': 0.17543859649122806, 'recall': 0.08403361344537816, 'f1': 0.11363636363636363, 'number': 119}  | {'precision': 0.66431718061674, 'recall': 0.707981220657277, 'f1': 0.6854545454545454, 'number': 1065}       | 0.6266            | 0.6804         | 0.6524     | 0.7606           |
+| 0.688         | 5.0   | 50   | 0.7202          | {'precision': 0.6440677966101694, 'recall': 0.7515451174289246, 'f1': 0.6936679977181973, 'number': 809}  | {'precision': 0.345679012345679, 'recall': 0.23529411764705882, 'f1': 0.27999999999999997, 'number': 119}    | {'precision': 0.6864197530864198, 'recall': 0.7830985915492957, 'f1': 0.7315789473684212, 'number': 1065}    | 0.6562            | 0.7376         | 0.6945     | 0.7799           |
+| 0.5806        | 6.0   | 60   | 0.6899          | {'precision': 0.6547368421052632, 'recall': 0.7688504326328801, 'f1': 0.7072200113700967, 'number': 809}  | {'precision': 0.3068181818181818, 'recall': 0.226890756302521, 'f1': 0.2608695652173913, 'number': 119}      | {'precision': 0.723441615452151, 'recall': 0.7737089201877935, 'f1': 0.7477313974591652, 'number': 1065}     | 0.6766            | 0.7391         | 0.7065     | 0.7841           |
+| 0.4975        | 7.0   | 70   | 0.6633          | {'precision': 0.6722689075630253, 'recall': 0.7911001236093943, 'f1': 0.7268597387847815, 'number': 809}  | {'precision': 0.30434782608695654, 'recall': 0.23529411764705882, 'f1': 0.2654028436018957, 'number': 119}   | {'precision': 0.7429577464788732, 'recall': 0.7924882629107981, 'f1': 0.7669241253975465, 'number': 1065}    | 0.6936            | 0.7587         | 0.7247     | 0.7969           |
+| 0.4425        | 8.0   | 80   | 0.6850          | {'precision': 0.6790123456790124, 'recall': 0.8158220024721878, 'f1': 0.7411566535654126, 'number': 809}  | {'precision': 0.3333333333333333, 'recall': 0.29411764705882354, 'f1': 0.3125, 'number': 119}                | {'precision': 0.7445319335083115, 'recall': 0.7990610328638498, 'f1': 0.7708333333333334, 'number': 1065}    | 0.6964            | 0.7757         | 0.7339     | 0.7933           |
+| 0.3867        | 9.0   | 90   | 0.6700          | {'precision': 0.7067254685777288, 'recall': 0.792336217552534, 'f1': 0.7470862470862472, 'number': 809}   | {'precision': 0.3181818181818182, 'recall': 0.29411764705882354, 'f1': 0.3056768558951965, 'number': 119}    | {'precision': 0.7567099567099567, 'recall': 0.8206572769953052, 'f1': 0.7873873873873873, 'number': 1065}    | 0.7136            | 0.7777         | 0.7443     | 0.8031           |
+| 0.3746        | 10.0  | 100  | 0.6724          | {'precision': 0.7029702970297029, 'recall': 0.7898640296662547, 'f1': 0.7438882421420255, 'number': 809}  | {'precision': 0.3425925925925926, 'recall': 0.31092436974789917, 'f1': 0.3259911894273128, 'number': 119}    | {'precision': 0.7665505226480837, 'recall': 0.8262910798122066, 'f1': 0.7953004970628107, 'number': 1065}    | 0.7187            | 0.7807         | 0.7484     | 0.8095           |
+| 0.3198        | 11.0  | 110  | 0.6931          | {'precision': 0.7155266015200868, 'recall': 0.8145859085290482, 'f1': 0.761849710982659, 'number': 809}   | {'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}   | {'precision': 0.7760279965004374, 'recall': 0.8328638497652582, 'f1': 0.8034420289855072, 'number': 1065}    | 0.7237            | 0.7953         | 0.7578     | 0.8042           |
+| 0.3041        | 12.0  | 120  | 0.6943          | {'precision': 0.7182628062360802, 'recall': 0.7972805933250927, 'f1': 0.7557117750439368, 'number': 809}  | {'precision': 0.33884297520661155, 'recall': 0.3445378151260504, 'f1': 0.3416666666666667, 'number': 119}    | {'precision': 0.780053428317008, 'recall': 0.8225352112676056, 'f1': 0.8007312614259597, 'number': 1065}     | 0.7292            | 0.7837         | 0.7555     | 0.8068           |
+| 0.2828        | 13.0  | 130  | 0.7021          | {'precision': 0.7139737991266376, 'recall': 0.8084054388133498, 'f1': 0.7582608695652174, 'number': 809}  | {'precision': 0.358974358974359, 'recall': 0.35294117647058826, 'f1': 0.35593220338983056, 'number': 119}    | {'precision': 0.8, 'recall': 0.8413145539906103, 'f1': 0.820137299771167, 'number': 1065}                    | 0.7394            | 0.7988         | 0.7680     | 0.8069           |
+| 0.2647        | 14.0  | 140  | 0.7052          | {'precision': 0.7184035476718403, 'recall': 0.8009888751545118, 'f1': 0.7574517825832847, 'number': 809}  | {'precision': 0.3282442748091603, 'recall': 0.36134453781512604, 'f1': 0.344, 'number': 119}                 | {'precision': 0.7872340425531915, 'recall': 0.8338028169014085, 'f1': 0.8098495212038304, 'number': 1065}    | 0.7307            | 0.7923         | 0.7602     | 0.8076           |
+| 0.265         | 15.0  | 150  | 0.7033          | {'precision': 0.7135016465422612, 'recall': 0.8034610630407911, 'f1': 0.7558139534883722, 'number': 809}  | {'precision': 0.34959349593495936, 'recall': 0.36134453781512604, 'f1': 0.35537190082644626, 'number': 119}  | {'precision': 0.7895204262877442, 'recall': 0.8347417840375587, 'f1': 0.8115015974440895, 'number': 1065}    | 0.7324            | 0.7938         | 0.7619     | 0.8096           |
+
+
+### Framework versions
+
+- Transformers 4.46.3
+- Pytorch 2.5.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.20.3

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