Training in progress, epoch 1

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: 0.6859
+- Answer: {'precision': 0.7175572519083969, 'recall': 0.8133498145859085, 'f1': 0.7624565469293164, 'number': 809}
+- Header: {'precision': 0.29411764705882354, 'recall': 0.33613445378151263, 'f1': 0.3137254901960785, 'number': 119}
+- Question: {'precision': 0.7724867724867724, 'recall': 0.8225352112676056, 'f1': 0.7967257844474761, 'number': 1065}
+- Overall Precision: 0.7197
+- Overall Recall: 0.7898
+- Overall F1: 0.7531
+- 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: 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8268        | 1.0   | 10   | 1.5857          | {'precision': 0.015523932729624839, 'recall': 0.014833127317676144, 'f1': 0.015170670037926676, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.17011834319526628, 'recall': 0.107981220657277, 'f1': 0.1321079839172889, 'number': 1065}  | 0.0876            | 0.0637         | 0.0738     | 0.3586           |
+| 1.4514        | 2.0   | 20   | 1.2482          | {'precision': 0.28865979381443296, 'recall': 0.311495673671199, 'f1': 0.29964328180737215, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.38357142857142856, 'recall': 0.504225352112676, 'f1': 0.43569979716024343, 'number': 1065} | 0.3471            | 0.3959         | 0.3699     | 0.5859           |
+| 1.1188        | 3.0   | 30   | 0.9477          | {'precision': 0.5157232704402516, 'recall': 0.6081582200247219, 'f1': 0.5581395348837209, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.5390879478827362, 'recall': 0.6215962441314554, 'f1': 0.5774095071958134, 'number': 1065}  | 0.5215            | 0.5790         | 0.5487     | 0.7076           |
+| 0.8437        | 4.0   | 40   | 0.7798          | {'precision': 0.5986124876114965, 'recall': 0.7466007416563659, 'f1': 0.6644664466446645, 'number': 809}       | {'precision': 0.1875, 'recall': 0.07563025210084033, 'f1': 0.10778443113772454, 'number': 119}             | {'precision': 0.6486718080548415, 'recall': 0.7107981220657277, 'f1': 0.6783154121863798, 'number': 1065}  | 0.6160            | 0.6874         | 0.6498     | 0.7580           |
+| 0.6804        | 5.0   | 50   | 0.7073          | {'precision': 0.6413502109704642, 'recall': 0.7515451174289246, 'f1': 0.6920887877063175, 'number': 809}       | {'precision': 0.3, 'recall': 0.17647058823529413, 'f1': 0.22222222222222224, 'number': 119}                | {'precision': 0.6712662337662337, 'recall': 0.7765258215962442, 'f1': 0.7200696560731389, 'number': 1065}  | 0.6471            | 0.7306         | 0.6863     | 0.7850           |
+| 0.5726        | 6.0   | 60   | 0.6805          | {'precision': 0.643141153081511, 'recall': 0.799752781211372, 'f1': 0.7129476584022039, 'number': 809}         | {'precision': 0.3142857142857143, 'recall': 0.18487394957983194, 'f1': 0.23280423280423282, 'number': 119} | {'precision': 0.709372312983663, 'recall': 0.7746478873239436, 'f1': 0.7405745062836624, 'number': 1065}   | 0.6673            | 0.7496         | 0.7060     | 0.7854           |
+| 0.5005        | 7.0   | 70   | 0.6536          | {'precision': 0.6701680672268907, 'recall': 0.788627935723115, 'f1': 0.7245883021010789, 'number': 809}        | {'precision': 0.27450980392156865, 'recall': 0.23529411764705882, 'f1': 0.2533936651583711, 'number': 119} | {'precision': 0.743103448275862, 'recall': 0.8093896713615023, 'f1': 0.7748314606741572, 'number': 1065}   | 0.6902            | 0.7667         | 0.7264     | 0.7982           |
+| 0.444         | 8.0   | 80   | 0.6526          | {'precision': 0.6802935010482181, 'recall': 0.8022249690976514, 'f1': 0.7362450368689732, 'number': 809}       | {'precision': 0.26956521739130435, 'recall': 0.2605042016806723, 'f1': 0.264957264957265, 'number': 119}   | {'precision': 0.7400690846286702, 'recall': 0.8046948356807512, 'f1': 0.7710301394511921, 'number': 1065}  | 0.6902            | 0.7712         | 0.7284     | 0.8022           |
+| 0.3904        | 9.0   | 90   | 0.6549          | {'precision': 0.6905781584582441, 'recall': 0.7972805933250927, 'f1': 0.7401032702237521, 'number': 809}       | {'precision': 0.26666666666666666, 'recall': 0.2689075630252101, 'f1': 0.26778242677824265, 'number': 119} | {'precision': 0.7554019014693172, 'recall': 0.8206572769953052, 'f1': 0.7866786678667866, 'number': 1065}  | 0.7015            | 0.7782         | 0.7379     | 0.8073           |
+| 0.3778        | 10.0  | 100  | 0.6593          | {'precision': 0.6996805111821086, 'recall': 0.8121137206427689, 'f1': 0.7517162471395881, 'number': 809}       | {'precision': 0.3018867924528302, 'recall': 0.2689075630252101, 'f1': 0.28444444444444444, 'number': 119}  | {'precision': 0.7707231040564374, 'recall': 0.8206572769953052, 'f1': 0.7949067758071852, 'number': 1065}  | 0.7173            | 0.7842         | 0.7493     | 0.8096           |
+| 0.3205        | 11.0  | 110  | 0.6673          | {'precision': 0.7185104052573932, 'recall': 0.8108776266996292, 'f1': 0.761904761904762, 'number': 809}        | {'precision': 0.26277372262773724, 'recall': 0.3025210084033613, 'f1': 0.28125000000000006, 'number': 119} | {'precision': 0.7557643040136636, 'recall': 0.8309859154929577, 'f1': 0.7915921288014313, 'number': 1065}  | 0.7100            | 0.7913         | 0.7485     | 0.8077           |
+| 0.3107        | 12.0  | 120  | 0.6723          | {'precision': 0.7185104052573932, 'recall': 0.8108776266996292, 'f1': 0.761904761904762, 'number': 809}        | {'precision': 0.2803030303030303, 'recall': 0.31092436974789917, 'f1': 0.29482071713147406, 'number': 119} | {'precision': 0.7740213523131673, 'recall': 0.8169014084507042, 'f1': 0.7948835084513477, 'number': 1065}  | 0.7206            | 0.7842         | 0.7511     | 0.8102           |
+| 0.2906        | 13.0  | 130  | 0.6774          | {'precision': 0.7175324675324676, 'recall': 0.8195302843016069, 'f1': 0.7651471436814773, 'number': 809}       | {'precision': 0.2824427480916031, 'recall': 0.31092436974789917, 'f1': 0.29600000000000004, 'number': 119} | {'precision': 0.7678883071553229, 'recall': 0.8262910798122066, 'f1': 0.7960199004975125, 'number': 1065}  | 0.7179            | 0.7928         | 0.7535     | 0.8111           |
+| 0.2684        | 14.0  | 140  | 0.6829          | {'precision': 0.716304347826087, 'recall': 0.8145859085290482, 'f1': 0.7622903412377097, 'number': 809}        | {'precision': 0.2900763358778626, 'recall': 0.31932773109243695, 'f1': 0.304, 'number': 119}               | {'precision': 0.7742504409171076, 'recall': 0.8244131455399061, 'f1': 0.7985447930877672, 'number': 1065}  | 0.7208            | 0.7903         | 0.7539     | 0.8115           |
+| 0.2659        | 15.0  | 150  | 0.6859          | {'precision': 0.7175572519083969, 'recall': 0.8133498145859085, 'f1': 0.7624565469293164, 'number': 809}       | {'precision': 0.29411764705882354, 'recall': 0.33613445378151263, 'f1': 0.3137254901960785, 'number': 119} | {'precision': 0.7724867724867724, 'recall': 0.8225352112676056, 'f1': 0.7967257844474761, 'number': 1065}  | 0.7197            | 0.7898         | 0.7531     | 0.8101           |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.3.0+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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+}