End of training
Browse files- README.md +76 -0
- logs/events.out.tfevents.1682547774.datascience-1-0-ml-g4dn-xlarge-94fad2f4401e538ca1255dfa1e84.1328.0 +2 -2
- logs/events.out.tfevents.1682551221.datascience-1-0-ml-g4dn-xlarge-94fad2f4401e538ca1255dfa1e84.1328.2 +3 -0
- merges.txt +0 -0
- preprocessor_config.json +26 -0
- special_tokens_map.json +51 -0
- tokenizer.json +0 -0
- tokenizer_config.json +85 -0
- vocab.json +0 -0
README.md
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---
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license: mit
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tags:
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- generated_from_trainer
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datasets:
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- funsd-layoutlmv3
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model-index:
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- name: lilt-en-funsd
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# lilt-en-funsd
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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.
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It achieves the following results on the evaluation set:
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- Loss: 1.7343
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- Answer: {'precision': 0.865979381443299, 'recall': 0.9253365973072215, 'f1': 0.8946745562130176, 'number': 817}
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- Header: {'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119}
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- Question: {'precision': 0.8937329700272479, 'recall': 0.9136490250696379, 'f1': 0.9035812672176309, 'number': 1077}
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- Overall Precision: 0.8696
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- Overall Recall: 0.8977
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- Overall F1: 0.8834
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- Overall Accuracy: 0.8048
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- training_steps: 2500
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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| 0.4108 | 10.53 | 200 | 0.9777 | {'precision': 0.8268590455049944, 'recall': 0.9118727050183598, 'f1': 0.8672875436554133, 'number': 817} | {'precision': 0.6464646464646465, 'recall': 0.5378151260504201, 'f1': 0.5871559633027523, 'number': 119} | {'precision': 0.8931860036832413, 'recall': 0.9006499535747446, 'f1': 0.8969024503005086, 'number': 1077} | 0.8528 | 0.8838 | 0.8680 | 0.8028 |
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| 0.0416 | 21.05 | 400 | 1.3626 | {'precision': 0.8482446206115515, 'recall': 0.9167686658506732, 'f1': 0.8811764705882352, 'number': 817} | {'precision': 0.5266666666666666, 'recall': 0.6638655462184874, 'f1': 0.5873605947955389, 'number': 119} | {'precision': 0.891566265060241, 'recall': 0.89322191272052, 'f1': 0.8923933209647495, 'number': 1077} | 0.8475 | 0.8892 | 0.8679 | 0.8033 |
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| 0.017 | 31.58 | 600 | 1.4784 | {'precision': 0.8646341463414634, 'recall': 0.8678090575275398, 'f1': 0.8662186927306048, 'number': 817} | {'precision': 0.6486486486486487, 'recall': 0.6050420168067226, 'f1': 0.6260869565217391, 'number': 119} | {'precision': 0.8519148936170213, 'recall': 0.9294336118848654, 'f1': 0.8889875666074601, 'number': 1077} | 0.8462 | 0.8852 | 0.8653 | 0.7964 |
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| 0.0098 | 42.11 | 800 | 1.4397 | {'precision': 0.8267543859649122, 'recall': 0.9228886168910648, 'f1': 0.8721804511278195, 'number': 817} | {'precision': 0.5865384615384616, 'recall': 0.5126050420168067, 'f1': 0.5470852017937219, 'number': 119} | {'precision': 0.8928247048138056, 'recall': 0.9127205199628597, 'f1': 0.9026629935720845, 'number': 1077} | 0.8493 | 0.8932 | 0.8707 | 0.8077 |
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| 0.004 | 52.63 | 1000 | 1.5432 | {'precision': 0.8721893491124261, 'recall': 0.9020807833537332, 'f1': 0.8868832731648616, 'number': 817} | {'precision': 0.5681818181818182, 'recall': 0.6302521008403361, 'f1': 0.597609561752988, 'number': 119} | {'precision': 0.8956999085086916, 'recall': 0.9090064995357474, 'f1': 0.9023041474654377, 'number': 1077} | 0.8652 | 0.8897 | 0.8773 | 0.8162 |
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| 0.0025 | 63.16 | 1200 | 1.6970 | {'precision': 0.8733727810650888, 'recall': 0.9033047735618115, 'f1': 0.888086642599278, 'number': 817} | {'precision': 0.7215189873417721, 'recall': 0.4789915966386555, 'f1': 0.5757575757575758, 'number': 119} | {'precision': 0.8804251550044287, 'recall': 0.9229340761374187, 'f1': 0.901178603807797, 'number': 1077} | 0.8714 | 0.8887 | 0.8800 | 0.7970 |
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| 0.0012 | 73.68 | 1400 | 1.6351 | {'precision': 0.8643274853801169, 'recall': 0.9045287637698899, 'f1': 0.8839712918660287, 'number': 817} | {'precision': 0.6115702479338843, 'recall': 0.6218487394957983, 'f1': 0.6166666666666667, 'number': 119} | {'precision': 0.8899821109123435, 'recall': 0.9238625812441968, 'f1': 0.9066059225512528, 'number': 1077} | 0.8634 | 0.8982 | 0.8804 | 0.8059 |
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| 0.0006 | 84.21 | 1600 | 1.5729 | {'precision': 0.8616279069767442, 'recall': 0.9069767441860465, 'f1': 0.8837209302325582, 'number': 817} | {'precision': 0.6973684210526315, 'recall': 0.44537815126050423, 'f1': 0.5435897435897435, 'number': 119} | {'precision': 0.878868258178603, 'recall': 0.9229340761374187, 'f1': 0.9003623188405797, 'number': 1077} | 0.8650 | 0.8882 | 0.8765 | 0.8149 |
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| 0.0008 | 94.74 | 1800 | 1.8110 | {'precision': 0.8455467869222097, 'recall': 0.9179926560587516, 'f1': 0.880281690140845, 'number': 817} | {'precision': 0.5522388059701493, 'recall': 0.6218487394957983, 'f1': 0.5849802371541502, 'number': 119} | {'precision': 0.9101851851851852, 'recall': 0.9127205199628597, 'f1': 0.9114510894761243, 'number': 1077} | 0.8601 | 0.8977 | 0.8785 | 0.7979 |
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| 0.0003 | 105.26 | 2000 | 1.7278 | {'precision': 0.8635321100917431, 'recall': 0.9216646266829865, 'f1': 0.8916518650088809, 'number': 817} | {'precision': 0.591304347826087, 'recall': 0.5714285714285714, 'f1': 0.5811965811965812, 'number': 119} | {'precision': 0.8986301369863013, 'recall': 0.9136490250696379, 'f1': 0.9060773480662985, 'number': 1077} | 0.8670 | 0.8967 | 0.8816 | 0.8005 |
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| 0.0004 | 115.79 | 2200 | 1.7088 | {'precision': 0.8802816901408451, 'recall': 0.9179926560587516, 'f1': 0.8987417615338527, 'number': 817} | {'precision': 0.6120689655172413, 'recall': 0.5966386554621849, 'f1': 0.6042553191489363, 'number': 119} | {'precision': 0.8869801084990958, 'recall': 0.9108635097493036, 'f1': 0.8987631699496106, 'number': 1077} | 0.8689 | 0.8952 | 0.8818 | 0.8051 |
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| 0.0003 | 126.32 | 2400 | 1.7343 | {'precision': 0.865979381443299, 'recall': 0.9253365973072215, 'f1': 0.8946745562130176, 'number': 817} | {'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119} | {'precision': 0.8937329700272479, 'recall': 0.9136490250696379, 'f1': 0.9035812672176309, 'number': 1077} | 0.8696 | 0.8977 | 0.8834 | 0.8048 |
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### Framework versions
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- Transformers 4.28.1
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- Pytorch 1.13.1+cu117
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- Datasets 2.11.0
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- Tokenizers 0.13.3
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logs/events.out.tfevents.1682547774.datascience-1-0-ml-g4dn-xlarge-94fad2f4401e538ca1255dfa1e84.1328.0
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merges.txt
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preprocessor_config.json
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special_tokens_map.json
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tokenizer.json
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tokenizer_config.json
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+
0
|
58 |
+
],
|
59 |
+
"pad_token_label": -100,
|
60 |
+
"processor_class": "LayoutLMv3Processor",
|
61 |
+
"sep_token": {
|
62 |
+
"__type": "AddedToken",
|
63 |
+
"content": "</s>",
|
64 |
+
"lstrip": false,
|
65 |
+
"normalized": true,
|
66 |
+
"rstrip": false,
|
67 |
+
"single_word": false
|
68 |
+
},
|
69 |
+
"sep_token_box": [
|
70 |
+
0,
|
71 |
+
0,
|
72 |
+
0,
|
73 |
+
0
|
74 |
+
],
|
75 |
+
"tokenizer_class": "LayoutLMv3Tokenizer",
|
76 |
+
"trim_offsets": true,
|
77 |
+
"unk_token": {
|
78 |
+
"__type": "AddedToken",
|
79 |
+
"content": "<unk>",
|
80 |
+
"lstrip": false,
|
81 |
+
"normalized": true,
|
82 |
+
"rstrip": false,
|
83 |
+
"single_word": false
|
84 |
+
}
|
85 |
+
}
|
vocab.json
ADDED
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|
|