End of training

README.md

@@ -0,0 +1,79 @@
+---
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: ananth-docai1
+  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. -->
+
+# ananth-docai1
+
+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: 0.7024
+- Answer: {'precision': 0.7113513513513513, 'recall': 0.8133498145859085, 'f1': 0.7589388696655133, 'number': 809}
+- Header: {'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}
+- Question: {'precision': 0.7811387900355872, 'recall': 0.8244131455399061, 'f1': 0.8021927820922796, 'number': 1065}
+- Overall Precision: 0.7241
+- Overall Recall: 0.7903
+- Overall F1: 0.7558
+- Overall Accuracy: 0.8106
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7944        | 1.0   | 10   | 1.6233          | {'precision': 0.01929260450160772, 'recall': 0.014833127317676144, 'f1': 0.016771488469601678, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.27685325264750377, 'recall': 0.17183098591549295, 'f1': 0.2120509849362688, 'number': 1065} | 0.1520            | 0.0978         | 0.1190     | 0.3505           |
+| 1.5001        | 2.0   | 20   | 1.2971          | {'precision': 0.11125, 'recall': 0.1100123609394314, 'f1': 0.11062771908017402, 'number': 809}                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.4044059795436664, 'recall': 0.48262910798122066, 'f1': 0.4400684931506849, 'number': 1065}  | 0.2912            | 0.3026         | 0.2968     | 0.5348           |
+| 1.136         | 3.0   | 30   | 0.9852          | {'precision': 0.4911699779249448, 'recall': 0.5500618046971569, 'f1': 0.5189504373177842, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.6086587436332768, 'recall': 0.6732394366197183, 'f1': 0.6393223361569326, 'number': 1065}   | 0.5562            | 0.5830         | 0.5693     | 0.6941           |
+| 0.8567        | 4.0   | 40   | 0.8143          | {'precision': 0.627744510978044, 'recall': 0.7775030902348579, 'f1': 0.6946438431805633, 'number': 809}       | {'precision': 0.06666666666666667, 'recall': 0.01680672268907563, 'f1': 0.026845637583892617, 'number': 119} | {'precision': 0.6987179487179487, 'recall': 0.7164319248826291, 'f1': 0.7074640704682429, 'number': 1065}   | 0.6563            | 0.6994         | 0.6772     | 0.7467           |
+| 0.6998        | 5.0   | 50   | 0.7133          | {'precision': 0.6534859521331946, 'recall': 0.7762669962917181, 'f1': 0.7096045197740113, 'number': 809}      | {'precision': 0.2, 'recall': 0.11764705882352941, 'f1': 0.14814814814814817, 'number': 119}                  | {'precision': 0.7243532560214094, 'recall': 0.7624413145539906, 'f1': 0.7429094236047575, 'number': 1065}   | 0.6757            | 0.7296         | 0.7016     | 0.7781           |
+| 0.5886        | 6.0   | 60   | 0.6775          | {'precision': 0.648406374501992, 'recall': 0.8046971569839307, 'f1': 0.7181467181467182, 'number': 809}       | {'precision': 0.25806451612903225, 'recall': 0.13445378151260504, 'f1': 0.17679558011049723, 'number': 119}  | {'precision': 0.712947189097104, 'recall': 0.7859154929577464, 'f1': 0.7476552032157214, 'number': 1065}    | 0.6714            | 0.7546         | 0.7106     | 0.7890           |
+| 0.5185        | 7.0   | 70   | 0.6770          | {'precision': 0.6755888650963597, 'recall': 0.7799752781211372, 'f1': 0.7240390131956398, 'number': 809}      | {'precision': 0.2079207920792079, 'recall': 0.17647058823529413, 'f1': 0.19090909090909092, 'number': 119}   | {'precision': 0.7341337907375644, 'recall': 0.8037558685446009, 'f1': 0.7673688928731511, 'number': 1065}   | 0.6851            | 0.7566         | 0.7191     | 0.7955           |
+| 0.4672        | 8.0   | 80   | 0.6729          | {'precision': 0.683083511777302, 'recall': 0.788627935723115, 'f1': 0.7320711417096959, 'number': 809}        | {'precision': 0.23300970873786409, 'recall': 0.20168067226890757, 'f1': 0.21621621621621623, 'number': 119}  | {'precision': 0.747431506849315, 'recall': 0.819718309859155, 'f1': 0.7819077474249888, 'number': 1065}     | 0.6961            | 0.7702         | 0.7313     | 0.8007           |
+| 0.4188        | 9.0   | 90   | 0.6664          | {'precision': 0.6888888888888889, 'recall': 0.8046971569839307, 'f1': 0.74230330672748, 'number': 809}        | {'precision': 0.2727272727272727, 'recall': 0.25210084033613445, 'f1': 0.26200873362445415, 'number': 119}   | {'precision': 0.7708703374777975, 'recall': 0.8150234741784037, 'f1': 0.792332268370607, 'number': 1065}    | 0.7102            | 0.7772         | 0.7422     | 0.8045           |
+| 0.3724        | 10.0  | 100  | 0.6845          | {'precision': 0.6928721174004193, 'recall': 0.8170580964153276, 'f1': 0.7498581962563812, 'number': 809}      | {'precision': 0.33, 'recall': 0.2773109243697479, 'f1': 0.30136986301369867, 'number': 119}                  | {'precision': 0.7818343722172751, 'recall': 0.8244131455399061, 'f1': 0.8025594149908593, 'number': 1065}   | 0.7221            | 0.7888         | 0.7540     | 0.8047           |
+| 0.3402        | 11.0  | 110  | 0.6830          | {'precision': 0.7118093174431203, 'recall': 0.8121137206427689, 'f1': 0.7586605080831409, 'number': 809}      | {'precision': 0.3090909090909091, 'recall': 0.2857142857142857, 'f1': 0.296943231441048, 'number': 119}      | {'precision': 0.787422497785651, 'recall': 0.8347417840375587, 'f1': 0.8103919781221514, 'number': 1065}    | 0.7308            | 0.7928         | 0.7605     | 0.8129           |
+| 0.3219        | 12.0  | 120  | 0.6944          | {'precision': 0.7179203539823009, 'recall': 0.8022249690976514, 'f1': 0.7577349678925861, 'number': 809}      | {'precision': 0.3220338983050847, 'recall': 0.31932773109243695, 'f1': 0.32067510548523204, 'number': 119}   | {'precision': 0.781882145998241, 'recall': 0.8347417840375587, 'f1': 0.8074477747502271, 'number': 1065}    | 0.7300            | 0.7908         | 0.7592     | 0.8097           |
+| 0.3004        | 13.0  | 130  | 0.6978          | {'precision': 0.7147540983606557, 'recall': 0.8084054388133498, 'f1': 0.7587006960556845, 'number': 809}      | {'precision': 0.33043478260869563, 'recall': 0.31932773109243695, 'f1': 0.32478632478632474, 'number': 119}  | {'precision': 0.7890974084003575, 'recall': 0.8291079812206573, 'f1': 0.8086080586080587, 'number': 1065}   | 0.7329            | 0.7903         | 0.7605     | 0.8144           |
+| 0.2942        | 14.0  | 140  | 0.7001          | {'precision': 0.7145945945945946, 'recall': 0.8170580964153276, 'f1': 0.7623990772779701, 'number': 809}      | {'precision': 0.30708661417322836, 'recall': 0.3277310924369748, 'f1': 0.3170731707317073, 'number': 119}    | {'precision': 0.7820284697508897, 'recall': 0.8253521126760563, 'f1': 0.8031064412973961, 'number': 1065}   | 0.7256            | 0.7923         | 0.7575     | 0.8108           |
+| 0.2853        | 15.0  | 150  | 0.7024          | {'precision': 0.7113513513513513, 'recall': 0.8133498145859085, 'f1': 0.7589388696655133, 'number': 809}      | {'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}   | {'precision': 0.7811387900355872, 'recall': 0.8244131455399061, 'f1': 0.8021927820922796, 'number': 1065}   | 0.7241            | 0.7903         | 0.7558     | 0.8106           |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.1+cu117
+- Datasets 2.7.1
+- Tokenizers 0.13.2

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