End of training

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- funsd-layoutlmv3
+model-index:
+- name: lilt-en-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. -->
+
+# lilt-en-funsd
+
+This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.5496
+- Answer: {'precision': 0.875, 'recall': 0.9253365973072215, 'f1': 0.8994646044021416, 'number': 817}
+- Header: {'precision': 0.6276595744680851, 'recall': 0.4957983193277311, 'f1': 0.5539906103286385, 'number': 119}
+- Question: {'precision': 0.9049360146252285, 'recall': 0.9192200557103064, 'f1': 0.9120221096269001, 'number': 1077}
+- Overall Precision: 0.8796
+- Overall Recall: 0.8967
+- Overall F1: 0.8881
+- Overall Accuracy: 0.8134
+
+## 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: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 2500
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.434         | 10.53  | 200  | 1.0227          | {'precision': 0.8357705286839145, 'recall': 0.9094247246022031, 'f1': 0.8710433763188746, 'number': 817} | {'precision': 0.7058823529411765, 'recall': 0.40336134453781514, 'f1': 0.5133689839572192, 'number': 119} | {'precision': 0.8683522231909329, 'recall': 0.924791086350975, 'f1': 0.89568345323741, 'number': 1077}    | 0.8493            | 0.8877         | 0.8681     | 0.7935           |
+| 0.0484        | 21.05  | 400  | 1.3626          | {'precision': 0.8098360655737705, 'recall': 0.9069767441860465, 'f1': 0.8556581986143187, 'number': 817} | {'precision': 0.6086956521739131, 'recall': 0.47058823529411764, 'f1': 0.5308056872037914, 'number': 119} | {'precision': 0.8613333333333333, 'recall': 0.8997214484679665, 'f1': 0.8801089918256131, 'number': 1077} | 0.8283            | 0.8773         | 0.8521     | 0.7995           |
+| 0.0168        | 31.58  | 600  | 1.3003          | {'precision': 0.8440046565774156, 'recall': 0.8873929008567931, 'f1': 0.8651551312649164, 'number': 817} | {'precision': 0.6421052631578947, 'recall': 0.5126050420168067, 'f1': 0.5700934579439252, 'number': 119}  | {'precision': 0.8776595744680851, 'recall': 0.9192200557103064, 'f1': 0.8979591836734694, 'number': 1077} | 0.8530            | 0.8823         | 0.8674     | 0.8189           |
+| 0.008         | 42.11  | 800  | 1.3225          | {'precision': 0.8584795321637427, 'recall': 0.8984088127294981, 'f1': 0.8779904306220095, 'number': 817} | {'precision': 0.5736434108527132, 'recall': 0.6218487394957983, 'f1': 0.596774193548387, 'number': 119}   | {'precision': 0.888468809073724, 'recall': 0.872794800371402, 'f1': 0.8805620608899298, 'number': 1077}   | 0.8560            | 0.8684         | 0.8621     | 0.8210           |
+| 0.0059        | 52.63  | 1000 | 1.6362          | {'precision': 0.8307522123893806, 'recall': 0.9192166462668299, 'f1': 0.8727484020918072, 'number': 817} | {'precision': 0.6419753086419753, 'recall': 0.4369747899159664, 'f1': 0.52, 'number': 119}                | {'precision': 0.8944444444444445, 'recall': 0.8969359331476323, 'f1': 0.8956884561891516, 'number': 1077} | 0.8567            | 0.8788         | 0.8676     | 0.8061           |
+| 0.0027        | 63.16  | 1200 | 1.6927          | {'precision': 0.8269858541893362, 'recall': 0.9302325581395349, 'f1': 0.8755760368663594, 'number': 817} | {'precision': 0.6046511627906976, 'recall': 0.4369747899159664, 'f1': 0.5073170731707317, 'number': 119}  | {'precision': 0.9000925069380203, 'recall': 0.903435468895079, 'f1': 0.901760889712697, 'number': 1077}   | 0.8557            | 0.8867         | 0.8709     | 0.7939           |
+| 0.002         | 73.68  | 1400 | 1.4609          | {'precision': 0.8479467258601554, 'recall': 0.9351285189718482, 'f1': 0.889406286379511, 'number': 817}  | {'precision': 0.5726495726495726, 'recall': 0.5630252100840336, 'f1': 0.5677966101694915, 'number': 119}  | {'precision': 0.8917431192660551, 'recall': 0.9025069637883009, 'f1': 0.8970927549607752, 'number': 1077} | 0.8553            | 0.8957         | 0.8750     | 0.7965           |
+| 0.0012        | 84.21  | 1600 | 1.4851          | {'precision': 0.865909090909091, 'recall': 0.9326805385556916, 'f1': 0.8980553918680023, 'number': 817}  | {'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119}   | {'precision': 0.9008341056533827, 'recall': 0.9025069637883009, 'f1': 0.901669758812616, 'number': 1077}  | 0.8708            | 0.8937         | 0.8821     | 0.8131           |
+| 0.0006        | 94.74  | 1800 | 1.5228          | {'precision': 0.850613154960981, 'recall': 0.9339045287637698, 'f1': 0.8903150525087514, 'number': 817}  | {'precision': 0.594059405940594, 'recall': 0.5042016806722689, 'f1': 0.5454545454545453, 'number': 119}   | {'precision': 0.896709323583181, 'recall': 0.9108635097493036, 'f1': 0.9037309995393827, 'number': 1077}  | 0.8623            | 0.8962         | 0.8789     | 0.8082           |
+| 0.0004        | 105.26 | 2000 | 1.5287          | {'precision': 0.867579908675799, 'recall': 0.9302325581395349, 'f1': 0.8978145304193739, 'number': 817}  | {'precision': 0.6222222222222222, 'recall': 0.47058823529411764, 'f1': 0.5358851674641149, 'number': 119} | {'precision': 0.8917710196779964, 'recall': 0.9257195914577531, 'f1': 0.9084282460136676, 'number': 1077} | 0.8700            | 0.9006         | 0.8850     | 0.8128           |
+| 0.0003        | 115.79 | 2200 | 1.5306          | {'precision': 0.8766006984866124, 'recall': 0.9216646266829865, 'f1': 0.8985680190930787, 'number': 817} | {'precision': 0.6263736263736264, 'recall': 0.4789915966386555, 'f1': 0.5428571428571428, 'number': 119}  | {'precision': 0.8902765388046388, 'recall': 0.9266480965645311, 'f1': 0.908098271155596, 'number': 1077}  | 0.8730            | 0.8982         | 0.8854     | 0.8127           |
+| 0.0001        | 126.32 | 2400 | 1.5496          | {'precision': 0.875, 'recall': 0.9253365973072215, 'f1': 0.8994646044021416, 'number': 817}              | {'precision': 0.6276595744680851, 'recall': 0.4957983193277311, 'f1': 0.5539906103286385, 'number': 119}  | {'precision': 0.9049360146252285, 'recall': 0.9192200557103064, 'f1': 0.9120221096269001, 'number': 1077} | 0.8796            | 0.8967         | 0.8881     | 0.8134           |
+
+
+### Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.13.0
+- Tokenizers 0.13.3

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preprocessor_config.json

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special_tokens_map.json

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+{
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+    "content": "<s>",
+    "lstrip": false,
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+    "rstrip": false,
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tokenizer_config.json

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+}