End of training

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+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 an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.7516
+- Answer: {'precision': 0.8642691415313225, 'recall': 0.9118727050183598, 'f1': 0.8874329958308517, 'number': 817}
+- Header: {'precision': 0.6106194690265486, 'recall': 0.5798319327731093, 'f1': 0.5948275862068966, 'number': 119}
+- Question: {'precision': 0.9112149532710281, 'recall': 0.9052924791086351, 'f1': 0.9082440614811365, 'number': 1077}
+- Overall Precision: 0.8748
+- Overall Recall: 0.8887
+- Overall F1: 0.8817
+- Overall Accuracy: 0.8047
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.3967        | 10.53  | 200  | 1.2683          | {'precision': 0.8096256684491978, 'recall': 0.9265605875152999, 'f1': 0.8641552511415526, 'number': 817} | {'precision': 0.5242718446601942, 'recall': 0.453781512605042, 'f1': 0.4864864864864865, 'number': 119}   | {'precision': 0.9031339031339032, 'recall': 0.883008356545961, 'f1': 0.8929577464788733, 'number': 1077}  | 0.8427            | 0.8753         | 0.8587     | 0.7811           |
+| 0.0418        | 21.05  | 400  | 1.3042          | {'precision': 0.839390386869871, 'recall': 0.8763769889840881, 'f1': 0.8574850299401198, 'number': 817}  | {'precision': 0.46923076923076923, 'recall': 0.5126050420168067, 'f1': 0.4899598393574297, 'number': 119} | {'precision': 0.851721094439541, 'recall': 0.8960074280408542, 'f1': 0.8733031674208145, 'number': 1077}  | 0.8233            | 0.8654         | 0.8438     | 0.8022           |
+| 0.0152        | 31.58  | 600  | 1.3935          | {'precision': 0.8523255813953489, 'recall': 0.8971848225214198, 'f1': 0.8741800834824089, 'number': 817} | {'precision': 0.5865384615384616, 'recall': 0.5126050420168067, 'f1': 0.5470852017937219, 'number': 119}  | {'precision': 0.8781994704324801, 'recall': 0.9238625812441968, 'f1': 0.9004524886877827, 'number': 1077} | 0.8531            | 0.8887         | 0.8706     | 0.8109           |
+| 0.0071        | 42.11  | 800  | 1.5595          | {'precision': 0.857981220657277, 'recall': 0.8947368421052632, 'f1': 0.8759736369083283, 'number': 817}  | {'precision': 0.5957446808510638, 'recall': 0.47058823529411764, 'f1': 0.5258215962441314, 'number': 119} | {'precision': 0.8912058023572076, 'recall': 0.9127205199628597, 'f1': 0.9018348623853211, 'number': 1077} | 0.8638            | 0.8793         | 0.8715     | 0.8015           |
+| 0.0043        | 52.63  | 1000 | 1.5937          | {'precision': 0.835214446952596, 'recall': 0.9057527539779682, 'f1': 0.8690546095126248, 'number': 817}  | {'precision': 0.6145833333333334, 'recall': 0.4957983193277311, 'f1': 0.5488372093023256, 'number': 119}  | {'precision': 0.8835740072202166, 'recall': 0.9090064995357474, 'f1': 0.8961098398169337, 'number': 1077} | 0.8507            | 0.8833         | 0.8667     | 0.7973           |
+| 0.0018        | 63.16  | 1200 | 1.5940          | {'precision': 0.8645465253239105, 'recall': 0.8984088127294981, 'f1': 0.8811524609843937, 'number': 817} | {'precision': 0.5648854961832062, 'recall': 0.6218487394957983, 'f1': 0.5920000000000001, 'number': 119}  | {'precision': 0.8923357664233577, 'recall': 0.9080779944289693, 'f1': 0.9001380579843534, 'number': 1077} | 0.8603            | 0.8872         | 0.8736     | 0.8073           |
+| 0.0019        | 73.68  | 1400 | 1.6567          | {'precision': 0.860381861575179, 'recall': 0.8824969400244798, 'f1': 0.8712990936555891, 'number': 817}  | {'precision': 0.5462184873949579, 'recall': 0.5462184873949579, 'f1': 0.5462184873949579, 'number': 119}  | {'precision': 0.8730017761989343, 'recall': 0.9127205199628597, 'f1': 0.8924194280526555, 'number': 1077} | 0.8493            | 0.8788         | 0.8638     | 0.8039           |
+| 0.0009        | 84.21  | 1600 | 1.7442          | {'precision': 0.8505747126436781, 'recall': 0.9057527539779682, 'f1': 0.8772969768820391, 'number': 817} | {'precision': 0.6057692307692307, 'recall': 0.5294117647058824, 'f1': 0.5650224215246636, 'number': 119}  | {'precision': 0.8972477064220183, 'recall': 0.9080779944289693, 'f1': 0.9026303645592985, 'number': 1077} | 0.8629            | 0.8847         | 0.8737     | 0.7977           |
+| 0.001         | 94.74  | 1800 | 1.7450          | {'precision': 0.8391061452513966, 'recall': 0.9192166462668299, 'f1': 0.8773364485981309, 'number': 817} | {'precision': 0.5916666666666667, 'recall': 0.5966386554621849, 'f1': 0.5941422594142259, 'number': 119}  | {'precision': 0.9132075471698113, 'recall': 0.8987929433611885, 'f1': 0.9059429106223679, 'number': 1077} | 0.8627            | 0.8892         | 0.8757     | 0.7954           |
+| 0.0005        | 105.26 | 2000 | 1.7725          | {'precision': 0.8432919954904171, 'recall': 0.9155446756425949, 'f1': 0.8779342723004696, 'number': 817} | {'precision': 0.5964912280701754, 'recall': 0.5714285714285714, 'f1': 0.5836909871244635, 'number': 119}  | {'precision': 0.9066293183940243, 'recall': 0.9015784586815228, 'f1': 0.9040968342644321, 'number': 1077} | 0.8625            | 0.8877         | 0.8749     | 0.7995           |
+| 0.0002        | 115.79 | 2200 | 1.7327          | {'precision': 0.8607594936708861, 'recall': 0.9155446756425949, 'f1': 0.8873072360616844, 'number': 817} | {'precision': 0.6, 'recall': 0.5798319327731093, 'f1': 0.5897435897435898, 'number': 119}                 | {'precision': 0.9079925650557621, 'recall': 0.9071494893221913, 'f1': 0.9075708313980492, 'number': 1077} | 0.8709            | 0.8912         | 0.8809     | 0.8060           |
+| 0.0002        | 126.32 | 2400 | 1.7516          | {'precision': 0.8642691415313225, 'recall': 0.9118727050183598, 'f1': 0.8874329958308517, 'number': 817} | {'precision': 0.6106194690265486, 'recall': 0.5798319327731093, 'f1': 0.5948275862068966, 'number': 119}  | {'precision': 0.9112149532710281, 'recall': 0.9052924791086351, 'f1': 0.9082440614811365, 'number': 1077} | 0.8748            | 0.8887         | 0.8817     | 0.8047           |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 1.7.1+cpu
+- Datasets 2.19.1
+- 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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tokenizer_config.json

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+}