End of training

README.md

@@ -0,0 +1,78 @@
+---
+license: mit
+base_model: SCUT-DLVCLab/lilt-roberta-en-base
+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: 1.5541
+- Answer: {'precision': 0.8696655132641292, 'recall': 0.9228886168910648, 'f1': 0.8954869358669834, 'number': 817}
+- Header: {'precision': 0.6559139784946236, 'recall': 0.5126050420168067, 'f1': 0.5754716981132076, 'number': 119}
+- Question: {'precision': 0.9031963470319635, 'recall': 0.9182915506035283, 'f1': 0.9106813996316759, 'number': 1077}
+- Overall Precision: 0.8779
+- Overall Recall: 0.8962
+- Overall F1: 0.8869
+- Overall Accuracy: 0.8228
+
+## 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
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4364        | 10.53  | 200  | 0.9844          | {'precision': 0.834106728538283, 'recall': 0.8800489596083231, 'f1': 0.8564621798689696, 'number': 817}  | {'precision': 0.41935483870967744, 'recall': 0.6554621848739496, 'f1': 0.5114754098360655, 'number': 119} | {'precision': 0.8516833484986351, 'recall': 0.8690807799442897, 'f1': 0.860294117647059, 'number': 1077}  | 0.8072            | 0.8609         | 0.8332     | 0.7792           |
+| 0.0425        | 21.05  | 400  | 1.1086          | {'precision': 0.8603550295857988, 'recall': 0.8898408812729498, 'f1': 0.8748495788206979, 'number': 817} | {'precision': 0.6288659793814433, 'recall': 0.5126050420168067, 'f1': 0.5648148148148148, 'number': 119}  | {'precision': 0.8763250883392226, 'recall': 0.9210770659238626, 'f1': 0.8981439565414214, 'number': 1077} | 0.8582            | 0.8843         | 0.8711     | 0.8199           |
+| 0.0132        | 31.58  | 600  | 1.3613          | {'precision': 0.8785276073619632, 'recall': 0.8763769889840881, 'f1': 0.8774509803921567, 'number': 817} | {'precision': 0.5182481751824818, 'recall': 0.5966386554621849, 'f1': 0.5546875, 'number': 119}           | {'precision': 0.8656195462478184, 'recall': 0.9210770659238626, 'f1': 0.8924876293297346, 'number': 1077} | 0.8480            | 0.8838         | 0.8655     | 0.8090           |
+| 0.0061        | 42.11  | 800  | 1.5515          | {'precision': 0.8825, 'recall': 0.8641370869033048, 'f1': 0.8732220160791588, 'number': 817}             | {'precision': 0.5689655172413793, 'recall': 0.5546218487394958, 'f1': 0.5617021276595745, 'number': 119}  | {'precision': 0.8888888888888888, 'recall': 0.8987929433611885, 'f1': 0.8938134810710988, 'number': 1077} | 0.8678            | 0.8644         | 0.8661     | 0.7964           |
+| 0.0041        | 52.63  | 1000 | 1.5132          | {'precision': 0.8808664259927798, 'recall': 0.8959608323133414, 'f1': 0.8883495145631067, 'number': 817} | {'precision': 0.6296296296296297, 'recall': 0.5714285714285714, 'f1': 0.5991189427312775, 'number': 119}  | {'precision': 0.8793256433007985, 'recall': 0.9201485608170845, 'f1': 0.8992740471869328, 'number': 1077} | 0.8669            | 0.8897         | 0.8782     | 0.8021           |
+| 0.0023        | 63.16  | 1200 | 1.6099          | {'precision': 0.8483466362599772, 'recall': 0.9106487148102815, 'f1': 0.8783943329397875, 'number': 817} | {'precision': 0.6470588235294118, 'recall': 0.46218487394957986, 'f1': 0.5392156862745099, 'number': 119} | {'precision': 0.8795718108831401, 'recall': 0.9155060352831941, 'f1': 0.897179253867152, 'number': 1077}  | 0.8569            | 0.8867         | 0.8716     | 0.8007           |
+| 0.0013        | 73.68  | 1400 | 1.5668          | {'precision': 0.8819277108433735, 'recall': 0.8959608323133414, 'f1': 0.8888888888888888, 'number': 817} | {'precision': 0.5775862068965517, 'recall': 0.5630252100840336, 'f1': 0.5702127659574467, 'number': 119}  | {'precision': 0.8972477064220183, 'recall': 0.9080779944289693, 'f1': 0.9026303645592985, 'number': 1077} | 0.8728            | 0.8828         | 0.8777     | 0.8079           |
+| 0.0009        | 84.21  | 1600 | 1.7323          | {'precision': 0.8639534883720931, 'recall': 0.9094247246022031, 'f1': 0.8861061419200955, 'number': 817} | {'precision': 0.6039603960396039, 'recall': 0.5126050420168067, 'f1': 0.5545454545454545, 'number': 119}  | {'precision': 0.8962962962962963, 'recall': 0.8987929433611885, 'f1': 0.8975428836346777, 'number': 1077} | 0.8682            | 0.8803         | 0.8742     | 0.8078           |
+| 0.0008        | 94.74  | 1800 | 1.5326          | {'precision': 0.8741258741258742, 'recall': 0.9179926560587516, 'f1': 0.8955223880597015, 'number': 817} | {'precision': 0.6226415094339622, 'recall': 0.5546218487394958, 'f1': 0.5866666666666668, 'number': 119}  | {'precision': 0.9044117647058824, 'recall': 0.9136490250696379, 'f1': 0.9090069284064666, 'number': 1077} | 0.8772            | 0.8942         | 0.8856     | 0.8208           |
+| 0.0003        | 105.26 | 2000 | 1.5560          | {'precision': 0.8625429553264605, 'recall': 0.9216646266829865, 'f1': 0.8911242603550296, 'number': 817} | {'precision': 0.616822429906542, 'recall': 0.5546218487394958, 'f1': 0.5840707964601769, 'number': 119}   | {'precision': 0.9008264462809917, 'recall': 0.9108635097493036, 'f1': 0.9058171745152355, 'number': 1077} | 0.8700            | 0.8942         | 0.8819     | 0.8189           |
+| 0.0002        | 115.79 | 2200 | 1.5541          | {'precision': 0.8696655132641292, 'recall': 0.9228886168910648, 'f1': 0.8954869358669834, 'number': 817} | {'precision': 0.6559139784946236, 'recall': 0.5126050420168067, 'f1': 0.5754716981132076, 'number': 119}  | {'precision': 0.9031963470319635, 'recall': 0.9182915506035283, 'f1': 0.9106813996316759, 'number': 1077} | 0.8779            | 0.8962         | 0.8869     | 0.8228           |
+| 0.0002        | 126.32 | 2400 | 1.5664          | {'precision': 0.8670520231213873, 'recall': 0.9179926560587516, 'f1': 0.89179548156956, 'number': 817}   | {'precision': 0.6595744680851063, 'recall': 0.5210084033613446, 'f1': 0.5821596244131456, 'number': 119}  | {'precision': 0.9135687732342007, 'recall': 0.9127205199628597, 'f1': 0.913144449605202, 'number': 1077}  | 0.8821            | 0.8917         | 0.8869     | 0.8262           |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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