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rushabhGod
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lilt-en-funsd

Token Classification
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lilt
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  1. README.md +28 -27
  2. logs/events.out.tfevents.1737023912.PC2031.22320.0 +2 -2
  3. model.safetensors +1 -1
README.md CHANGED
@@ -1,4 +1,5 @@
1
  ---
 
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  license: mit
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  base_model: SCUT-DLVCLab/lilt-roberta-en-base
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  tags:
@@ -15,14 +16,14 @@ should probably proofread and complete it, then remove this comment. -->
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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 an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.7584
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- - Answer: {'precision': 0.8773364485981309, 'recall': 0.9192166462668299, 'f1': 0.8977884040645547, 'number': 817}
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- - Header: {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119}
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- - Question: {'precision': 0.9097605893186004, 'recall': 0.9173630454967502, 'f1': 0.9135460009246417, 'number': 1077}
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- - Overall Precision: 0.8833
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- - Overall Recall: 0.8952
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- - Overall F1: 0.8892
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- - Overall Accuracy: 0.8076
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  ## Model description
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@@ -45,32 +46,32 @@ The following hyperparameters were used during training:
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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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  - mixed_precision_training: Native AMP
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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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- | 0.3859 | 10.5263 | 200 | 1.1901 | {'precision': 0.8349514563106796, 'recall': 0.8421052631578947, 'f1': 0.8385131017672149, 'number': 817} | {'precision': 0.42953020134228187, 'recall': 0.5378151260504201, 'f1': 0.47761194029850745, 'number': 119} | {'precision': 0.8739946380697051, 'recall': 0.9080779944289693, 'f1': 0.8907103825136613, 'number': 1077} | 0.8270 | 0.8594 | 0.8429 | 0.7738 |
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- | 0.0461 | 21.0526 | 400 | 1.3985 | {'precision': 0.8586448598130841, 'recall': 0.8996328029375765, 'f1': 0.8786610878661089, 'number': 817} | {'precision': 0.5, 'recall': 0.6050420168067226, 'f1': 0.5475285171102661, 'number': 119} | {'precision': 0.8864468864468864, 'recall': 0.8987929433611885, 'f1': 0.8925772245274319, 'number': 1077} | 0.8485 | 0.8818 | 0.8648 | 0.7846 |
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- | 0.0139 | 31.5789 | 600 | 1.4340 | {'precision': 0.8617021276595744, 'recall': 0.8922888616891065, 'f1': 0.8767288033674082, 'number': 817} | {'precision': 0.5263157894736842, 'recall': 0.5882352941176471, 'f1': 0.5555555555555555, 'number': 119} | {'precision': 0.8739946380697051, 'recall': 0.9080779944289693, 'f1': 0.8907103825136613, 'number': 1077} | 0.8470 | 0.8828 | 0.8645 | 0.8029 |
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- | 0.0074 | 42.1053 | 800 | 1.5489 | {'precision': 0.8450057405281286, 'recall': 0.9008567931456548, 'f1': 0.8720379146919431, 'number': 817} | {'precision': 0.6344086021505376, 'recall': 0.4957983193277311, 'f1': 0.5566037735849056, 'number': 119} | {'precision': 0.8733738074588031, 'recall': 0.9350046425255338, 'f1': 0.9031390134529148, 'number': 1077} | 0.8512 | 0.8952 | 0.8726 | 0.7957 |
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- | 0.0026 | 52.6316 | 1000 | 1.6408 | {'precision': 0.8661800486618005, 'recall': 0.8714810281517748, 'f1': 0.8688224527150702, 'number': 817} | {'precision': 0.5932203389830508, 'recall': 0.5882352941176471, 'f1': 0.5907172995780592, 'number': 119} | {'precision': 0.8859964093357271, 'recall': 0.9164345403899722, 'f1': 0.9009584664536742, 'number': 1077} | 0.8612 | 0.8788 | 0.8699 | 0.7988 |
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- | 0.005 | 63.1579 | 1200 | 1.5299 | {'precision': 0.8518518518518519, 'recall': 0.9008567931456548, 'f1': 0.875669244497323, 'number': 817} | {'precision': 0.6407766990291263, 'recall': 0.5546218487394958, 'f1': 0.5945945945945947, 'number': 119} | {'precision': 0.883128295254833, 'recall': 0.9331476323119777, 'f1': 0.90744920993228, 'number': 1077} | 0.8584 | 0.8977 | 0.8776 | 0.8010 |
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- | 0.004 | 73.6842 | 1400 | 1.5962 | {'precision': 0.8402699662542182, 'recall': 0.9143206854345165, 'f1': 0.8757327080890973, 'number': 817} | {'precision': 0.5625, 'recall': 0.5294117647058824, 'f1': 0.5454545454545455, 'number': 119} | {'precision': 0.8923076923076924, 'recall': 0.9155060352831941, 'f1': 0.9037580201649862, 'number': 1077} | 0.8528 | 0.8922 | 0.8721 | 0.8084 |
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- | 0.0007 | 84.2105 | 1600 | 1.6587 | {'precision': 0.8458904109589042, 'recall': 0.9069767441860465, 'f1': 0.8753691671588896, 'number': 817} | {'precision': 0.6355140186915887, 'recall': 0.5714285714285714, 'f1': 0.6017699115044248, 'number': 119} | {'precision': 0.8946894689468947, 'recall': 0.9229340761374187, 'f1': 0.9085923217550275, 'number': 1077} | 0.8610 | 0.8957 | 0.8780 | 0.8051 |
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- | 0.0007 | 94.7368 | 1800 | 1.5919 | {'precision': 0.8495475113122172, 'recall': 0.9192166462668299, 'f1': 0.8830099941211051, 'number': 817} | {'precision': 0.6055045871559633, 'recall': 0.5546218487394958, 'f1': 0.5789473684210525, 'number': 119} | {'precision': 0.9030797101449275, 'recall': 0.9257195914577531, 'f1': 0.9142595139844107, 'number': 1077} | 0.8650 | 0.9011 | 0.8827 | 0.8102 |
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- | 0.0004 | 105.2632 | 2000 | 1.7501 | {'precision': 0.8614318706697459, 'recall': 0.9130966952264382, 'f1': 0.8865121806298276, 'number': 817} | {'precision': 0.6052631578947368, 'recall': 0.5798319327731093, 'f1': 0.5922746781115881, 'number': 119} | {'precision': 0.9106976744186046, 'recall': 0.9090064995357474, 'f1': 0.9098513011152416, 'number': 1077} | 0.8730 | 0.8912 | 0.8820 | 0.8070 |
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- | 0.0003 | 115.7895 | 2200 | 1.7584 | {'precision': 0.8773364485981309, 'recall': 0.9192166462668299, 'f1': 0.8977884040645547, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119} | {'precision': 0.9097605893186004, 'recall': 0.9173630454967502, 'f1': 0.9135460009246417, 'number': 1077} | 0.8833 | 0.8952 | 0.8892 | 0.8076 |
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- | 0.0001 | 126.3158 | 2400 | 1.7527 | {'precision': 0.8525714285714285, 'recall': 0.9130966952264382, 'f1': 0.8817966903073285, 'number': 817} | {'precision': 0.6237623762376238, 'recall': 0.5294117647058824, 'f1': 0.5727272727272728, 'number': 119} | {'precision': 0.8963963963963963, 'recall': 0.9238625812441968, 'f1': 0.9099222679469592, 'number': 1077} | 0.8648 | 0.8962 | 0.8802 | 0.8057 |
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  ### Framework versions
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- - Transformers 4.44.0
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- - Pytorch 2.4.0+cu118
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- - Datasets 2.21.0
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- - Tokenizers 0.19.1
 
1
  ---
2
+ library_name: transformers
3
  license: mit
4
  base_model: SCUT-DLVCLab/lilt-roberta-en-base
5
  tags:
 
16
 
17
  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.
18
  It achieves the following results on the evaluation set:
19
+ - Loss: 1.5739
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+ - Answer: {'precision': 0.8710433763188745, 'recall': 0.9094247246022031, 'f1': 0.8898203592814371, 'number': 817}
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+ - Header: {'precision': 0.6336633663366337, 'recall': 0.5378151260504201, 'f1': 0.5818181818181819, 'number': 119}
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+ - Question: {'precision': 0.8975521305530372, 'recall': 0.9192200557103064, 'f1': 0.9082568807339448, 'number': 1077}
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+ - Overall Precision: 0.8736
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+ - Overall Recall: 0.8927
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+ - Overall F1: 0.8830
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+ - Overall Accuracy: 0.8177
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28
  ## Model description
29
 
 
46
  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - training_steps: 2500
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  - mixed_precision_training: Native AMP
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54
  ### Training results
55
 
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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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+ | 0.3657 | 10.5263 | 200 | 0.9521 | {'precision': 0.8126361655773421, 'recall': 0.9130966952264382, 'f1': 0.859942363112392, 'number': 817} | {'precision': 0.5252525252525253, 'recall': 0.4369747899159664, 'f1': 0.47706422018348627, 'number': 119} | {'precision': 0.8796046720575023, 'recall': 0.9090064995357474, 'f1': 0.8940639269406392, 'number': 1077} | 0.8343 | 0.8828 | 0.8578 | 0.8059 |
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+ | 0.0448 | 21.0526 | 400 | 1.2063 | {'precision': 0.8845686512758202, 'recall': 0.8910648714810282, 'f1': 0.8878048780487805, 'number': 817} | {'precision': 0.5034965034965035, 'recall': 0.6050420168067226, 'f1': 0.549618320610687, 'number': 119} | {'precision': 0.8940092165898618, 'recall': 0.9006499535747446, 'f1': 0.8973172987974098, 'number': 1077} | 0.8630 | 0.8793 | 0.8711 | 0.8133 |
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+ | 0.0135 | 31.5789 | 600 | 1.3466 | {'precision': 0.8726190476190476, 'recall': 0.8971848225214198, 'f1': 0.8847314423657212, 'number': 817} | {'precision': 0.4900662251655629, 'recall': 0.6218487394957983, 'f1': 0.5481481481481482, 'number': 119} | {'precision': 0.8789808917197452, 'recall': 0.8969359331476323, 'f1': 0.8878676470588236, 'number': 1077} | 0.8483 | 0.8808 | 0.8642 | 0.8083 |
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+ | 0.0069 | 42.1053 | 800 | 1.3562 | {'precision': 0.8235294117647058, 'recall': 0.9082007343941249, 'f1': 0.8637951105937136, 'number': 817} | {'precision': 0.6413043478260869, 'recall': 0.4957983193277311, 'f1': 0.5592417061611374, 'number': 119} | {'precision': 0.8723981900452489, 'recall': 0.8950789229340761, 'f1': 0.8835930339138405, 'number': 1077} | 0.8413 | 0.8768 | 0.8587 | 0.8063 |
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+ | 0.0058 | 52.6316 | 1000 | 1.4131 | {'precision': 0.8688524590163934, 'recall': 0.9082007343941249, 'f1': 0.8880909634949131, 'number': 817} | {'precision': 0.6310679611650486, 'recall': 0.5462184873949579, 'f1': 0.5855855855855856, 'number': 119} | {'precision': 0.8767605633802817, 'recall': 0.924791086350975, 'f1': 0.9001355625847266, 'number': 1077} | 0.8614 | 0.8957 | 0.8782 | 0.8110 |
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+ | 0.0034 | 63.1579 | 1200 | 1.4398 | {'precision': 0.867699642431466, 'recall': 0.8910648714810282, 'f1': 0.8792270531400967, 'number': 817} | {'precision': 0.6288659793814433, 'recall': 0.5126050420168067, 'f1': 0.5648148148148148, 'number': 119} | {'precision': 0.8971533516988063, 'recall': 0.9071494893221913, 'f1': 0.9021237303785781, 'number': 1077} | 0.8721 | 0.8773 | 0.8747 | 0.8054 |
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+ | 0.0016 | 73.6842 | 1400 | 1.6692 | {'precision': 0.8520231213872832, 'recall': 0.9020807833537332, 'f1': 0.8763376932223542, 'number': 817} | {'precision': 0.6039603960396039, 'recall': 0.5126050420168067, 'f1': 0.5545454545454545, 'number': 119} | {'precision': 0.9039923954372624, 'recall': 0.883008356545961, 'f1': 0.8933771723813998, 'number': 1077} | 0.8667 | 0.8689 | 0.8678 | 0.7919 |
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+ | 0.001 | 84.2105 | 1600 | 1.6412 | {'precision': 0.846927374301676, 'recall': 0.9277845777233782, 'f1': 0.8855140186915887, 'number': 817} | {'precision': 0.6095238095238096, 'recall': 0.5378151260504201, 'f1': 0.5714285714285715, 'number': 119} | {'precision': 0.8877828054298642, 'recall': 0.9108635097493036, 'f1': 0.8991750687442712, 'number': 1077} | 0.8565 | 0.8957 | 0.8757 | 0.7982 |
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+ | 0.0006 | 94.7368 | 1800 | 1.5924 | {'precision': 0.8748538011695907, 'recall': 0.9155446756425949, 'f1': 0.8947368421052633, 'number': 817} | {'precision': 0.64, 'recall': 0.5378151260504201, 'f1': 0.5844748858447488, 'number': 119} | {'precision': 0.8945487042001787, 'recall': 0.9294336118848654, 'f1': 0.9116575591985429, 'number': 1077} | 0.8742 | 0.9006 | 0.8872 | 0.8193 |
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+ | 0.0004 | 105.2632 | 2000 | 1.5639 | {'precision': 0.8710433763188745, 'recall': 0.9094247246022031, 'f1': 0.8898203592814371, 'number': 817} | {'precision': 0.6274509803921569, 'recall': 0.5378151260504201, 'f1': 0.579185520361991, 'number': 119} | {'precision': 0.8928892889288929, 'recall': 0.9210770659238626, 'f1': 0.9067641681901281, 'number': 1077} | 0.8708 | 0.8937 | 0.8821 | 0.8218 |
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+ | 0.0002 | 115.7895 | 2200 | 1.5740 | {'precision': 0.8684516880093132, 'recall': 0.9130966952264382, 'f1': 0.8902147971360381, 'number': 817} | {'precision': 0.65, 'recall': 0.5462184873949579, 'f1': 0.593607305936073, 'number': 119} | {'precision': 0.8928247048138056, 'recall': 0.9127205199628597, 'f1': 0.9026629935720845, 'number': 1077} | 0.8709 | 0.8912 | 0.8809 | 0.8162 |
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+ | 0.0002 | 126.3158 | 2400 | 1.5739 | {'precision': 0.8710433763188745, 'recall': 0.9094247246022031, 'f1': 0.8898203592814371, 'number': 817} | {'precision': 0.6336633663366337, 'recall': 0.5378151260504201, 'f1': 0.5818181818181819, 'number': 119} | {'precision': 0.8975521305530372, 'recall': 0.9192200557103064, 'f1': 0.9082568807339448, 'number': 1077} | 0.8736 | 0.8927 | 0.8830 | 0.8177 |
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  ### Framework versions
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+ - Transformers 4.48.0
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+ - Pytorch 2.5.1+cpu
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+ - Datasets 3.2.0
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+ - Tokenizers 0.21.0
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