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lilt-en-funsd

Token Classification
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lilt
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End of training

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  1. README.md +23 -24
  2. logs/events.out.tfevents.1685045223.MSI.5457.0 +2 -2
  3. logs/events.out.tfevents.1685059843.MSI.5457.2 +3 -0
  4. pytorch_model.bin +1 -1
README.md CHANGED
@@ -3,7 +3,7 @@ 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: []
@@ -14,16 +14,15 @@ 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: 1.5849
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- - Answer: {'precision': 0.8488636363636364, 'recall': 0.9143206854345165, 'f1': 0.8803771361225692, 'number': 817}
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- - Header: {'precision': 0.5631067961165048, 'recall': 0.48739495798319327, 'f1': 0.5225225225225225, 'number': 119}
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- - Question: {'precision': 0.8887859128822985, 'recall': 0.8904363974001857, 'f1': 0.8896103896103896, 'number': 1077}
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- - Overall Precision: 0.8555
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- - Overall Recall: 0.8763
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- - Overall F1: 0.8658
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- - Overall Accuracy: 0.8017
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  ## Model description
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@@ -53,20 +52,20 @@ The following hyperparameters were used during training:
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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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- | 0.7834 | 2.67 | 200 | 0.5897 | {'precision': 0.8059536934950385, 'recall': 0.8947368421052632, 'f1': 0.8480278422273781, 'number': 817} | {'precision': 0.4069767441860465, 'recall': 0.29411764705882354, 'f1': 0.34146341463414637, 'number': 119} | {'precision': 0.8141447368421053, 'recall': 0.9192200557103064, 'f1': 0.8634976013955518, 'number': 1077} | 0.7949 | 0.8723 | 0.8318 | 0.7937 |
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- | 0.2848 | 5.33 | 400 | 0.8788 | {'precision': 0.8155136268343816, 'recall': 0.9522643818849449, 'f1': 0.8785996612083569, 'number': 817} | {'precision': 0.46218487394957986, 'recall': 0.46218487394957986, 'f1': 0.46218487394957986, 'number': 119} | {'precision': 0.8724584103512015, 'recall': 0.8765088207985144, 'f1': 0.8744789254284391, 'number': 1077} | 0.8246 | 0.8828 | 0.8527 | 0.7916 |
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- | 0.1446 | 8.0 | 600 | 1.0402 | {'precision': 0.8363844393592678, 'recall': 0.8947368421052632, 'f1': 0.8645771732702543, 'number': 817} | {'precision': 0.5894736842105263, 'recall': 0.47058823529411764, 'f1': 0.5233644859813084, 'number': 119} | {'precision': 0.8886861313868614, 'recall': 0.904363974001857, 'f1': 0.8964565117349288, 'number': 1077} | 0.8528 | 0.8748 | 0.8637 | 0.7891 |
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- | 0.0791 | 10.67 | 800 | 1.1878 | {'precision': 0.8659549228944247, 'recall': 0.8935128518971848, 'f1': 0.8795180722891566, 'number': 817} | {'precision': 0.48360655737704916, 'recall': 0.4957983193277311, 'f1': 0.4896265560165975, 'number': 119} | {'precision': 0.8647007805724197, 'recall': 0.9257195914577531, 'f1': 0.8941704035874439, 'number': 1077} | 0.8432 | 0.8872 | 0.8647 | 0.7973 |
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- | 0.0459 | 13.33 | 1000 | 1.3884 | {'precision': 0.8078175895765473, 'recall': 0.9106487148102815, 'f1': 0.856156501726122, 'number': 817} | {'precision': 0.5789473684210527, 'recall': 0.46218487394957986, 'f1': 0.514018691588785, 'number': 119} | {'precision': 0.8819702602230484, 'recall': 0.8811513463324049, 'f1': 0.8815606130980028, 'number': 1077} | 0.8356 | 0.8684 | 0.8516 | 0.7831 |
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- | 0.0278 | 16.0 | 1200 | 1.4707 | {'precision': 0.8415051311288484, 'recall': 0.9033047735618115, 'f1': 0.8713105076741442, 'number': 817} | {'precision': 0.5869565217391305, 'recall': 0.453781512605042, 'f1': 0.5118483412322274, 'number': 119} | {'precision': 0.8690685413005272, 'recall': 0.9182915506035283, 'f1': 0.8930022573363431, 'number': 1077} | 0.8453 | 0.8847 | 0.8646 | 0.7801 |
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- | 0.0105 | 18.67 | 1400 | 1.5749 | {'precision': 0.843069873997709, 'recall': 0.9008567931456548, 'f1': 0.8710059171597634, 'number': 817} | {'precision': 0.5426356589147286, 'recall': 0.5882352941176471, 'f1': 0.5645161290322581, 'number': 119} | {'precision': 0.8799630655586335, 'recall': 0.8848653667595172, 'f1': 0.8824074074074075, 'number': 1077} | 0.8436 | 0.8738 | 0.8585 | 0.7800 |
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- | 0.0083 | 21.33 | 1600 | 1.5530 | {'precision': 0.8551236749116607, 'recall': 0.8886168910648715, 'f1': 0.8715486194477792, 'number': 817} | {'precision': 0.5727272727272728, 'recall': 0.5294117647058824, 'f1': 0.5502183406113538, 'number': 119} | {'precision': 0.8669032830523514, 'recall': 0.9071494893221913, 'f1': 0.8865698729582577, 'number': 1077} | 0.8466 | 0.8773 | 0.8617 | 0.8007 |
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- | 0.0045 | 24.0 | 1800 | 1.5849 | {'precision': 0.8488636363636364, 'recall': 0.9143206854345165, 'f1': 0.8803771361225692, 'number': 817} | {'precision': 0.5631067961165048, 'recall': 0.48739495798319327, 'f1': 0.5225225225225225, 'number': 119} | {'precision': 0.8887859128822985, 'recall': 0.8904363974001857, 'f1': 0.8896103896103896, 'number': 1077} | 0.8555 | 0.8763 | 0.8658 | 0.8017 |
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- | 0.0025 | 26.67 | 2000 | 1.6119 | {'precision': 0.8464203233256351, 'recall': 0.8971848225214198, 'f1': 0.8710635769459298, 'number': 817} | {'precision': 0.5566037735849056, 'recall': 0.4957983193277311, 'f1': 0.5244444444444444, 'number': 119} | {'precision': 0.8709386281588448, 'recall': 0.8960074280408542, 'f1': 0.8832951945080092, 'number': 1077} | 0.8447 | 0.8728 | 0.8585 | 0.7914 |
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- | 0.0019 | 29.33 | 2200 | 1.5579 | {'precision': 0.8661971830985915, 'recall': 0.9033047735618115, 'f1': 0.884361893349311, 'number': 817} | {'precision': 0.5660377358490566, 'recall': 0.5042016806722689, 'f1': 0.5333333333333334, 'number': 119} | {'precision': 0.8654188948306596, 'recall': 0.9015784586815228, 'f1': 0.8831286948613006, 'number': 1077} | 0.8505 | 0.8788 | 0.8644 | 0.7971 |
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- | 0.0008 | 32.0 | 2400 | 1.5983 | {'precision': 0.8530424799081515, 'recall': 0.9094247246022031, 'f1': 0.8803317535545023, 'number': 817} | {'precision': 0.5714285714285714, 'recall': 0.5042016806722689, 'f1': 0.5357142857142857, 'number': 119} | {'precision': 0.8768248175182481, 'recall': 0.8922934076137419, 'f1': 0.8844914864242981, 'number': 1077} | 0.8514 | 0.8763 | 0.8636 | 0.7982 |
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  ### Framework versions
 
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  tags:
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  - generated_from_trainer
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  datasets:
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+ - mydata
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  model-index:
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  - name: lilt-en-funsd
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  results: []
 
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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 mydata dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0000
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+ - In: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6}
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+ - Ear: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6}
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+ - Overall Precision: 1.0
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+ - Overall Recall: 1.0
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+ - Overall F1: 1.0
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+ - Overall Accuracy: 1.0
 
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | In | Ear | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:------:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 0.017 | 66.67 | 200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 133.33 | 400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 200.0 | 600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 266.67 | 800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 333.33 | 1000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 400.0 | 1200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 466.67 | 1400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 533.33 | 1600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 600.0 | 1800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 666.67 | 2000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 733.33 | 2200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 800.0 | 2400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 |
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  ### Framework versions
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