indudesane/Data_extraction
011
1---2license: mit3base_model: SCUT-DLVCLab/lilt-roberta-en-base4tags:5- generated_from_trainer6model-index:7- name: Data_extraction8 results: []9---10 11<!-- This model card has been generated automatically according to the information the Trainer had access to. You12should probably proofread and complete it, then remove this comment. -->13 14# Data_extraction15 16This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.17It achieves the following results on the evaluation set:18- Loss: 0.427719- Fsc Code: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22}20- Ame: {'precision': 0.391304347826087, 'recall': 0.42857142857142855, 'f1': 0.4090909090909091, 'number': 42}21- Ccount No: {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6}22- Ign: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}23- Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13}24- Ther: {'precision': 0.5287356321839081, 'recall': 0.5348837209302325, 'f1': 0.5317919075144507, 'number': 86}25- Overall Precision: 0.604526- Overall Recall: 0.614927- Overall F1: 0.609728- Overall Accuracy: 0.943129 30## Model description31 32More information needed33 34## Intended uses & limitations35 36More information needed37 38## Training and evaluation data39 40More information needed41 42## Training procedure43 44### Training hyperparameters45 46The following hyperparameters were used during training:47- learning_rate: 5e-0548- train_batch_size: 849- eval_batch_size: 850- seed: 4251- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0852- lr_scheduler_type: linear53- training_steps: 250054- mixed_precision_training: Native AMP55 56### Training results57 58| Training Loss | Epoch | Step | Validation Loss | Fsc Code | Ame | Ccount No | Ign | Mount | Ther | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |59|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|60| 0.1559 | 20.0 | 200 | 0.2349 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.3448275862068966, 'recall': 0.47619047619047616, 'f1': 0.39999999999999997, 'number': 42} | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.4329896907216495, 'recall': 0.4883720930232558, 'f1': 0.45901639344262296, 'number': 86} | 0.5155 | 0.5747 | 0.5435 | 0.9376 |61| 0.0138 | 40.0 | 400 | 0.2607 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.3148148148148148, 'recall': 0.40476190476190477, 'f1': 0.3541666666666667, 'number': 42} | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} | {'precision': 1.0, 'recall': 0.8, 'f1': 0.888888888888889, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.5, 'recall': 0.5465116279069767, 'f1': 0.5222222222222221, 'number': 86} | 0.5550 | 0.6092 | 0.5808 | 0.9372 |62| 0.0031 | 60.0 | 600 | 0.3808 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.2786885245901639, 'recall': 0.40476190476190477, 'f1': 0.33009708737864074, 'number': 42} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 6} | {'precision': 0.8333333333333334, 'recall': 1.0, 'f1': 0.9090909090909091, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.4077669902912621, 'recall': 0.4883720930232558, 'f1': 0.44444444444444436, 'number': 86} | 0.4928 | 0.5920 | 0.5379 | 0.9372 |63| 0.0031 | 80.0 | 800 | 0.3239 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.2807017543859649, 'recall': 0.38095238095238093, 'f1': 0.32323232323232326, 'number': 42} | {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.45, 'recall': 0.5232558139534884, 'f1': 0.48387096774193555, 'number': 86} | 0.5248 | 0.6092 | 0.5638 | 0.9532 |64| 0.0007 | 100.0 | 1000 | 0.3718 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.375, 'recall': 0.42857142857142855, 'f1': 0.39999999999999997, 'number': 42} | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 6} | {'precision': 0.8333333333333334, 'recall': 1.0, 'f1': 0.9090909090909091, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.4891304347826087, 'recall': 0.5232558139534884, 'f1': 0.5056179775280899, 'number': 86} | 0.5722 | 0.6149 | 0.5928 | 0.9467 |65| 0.0002 | 120.0 | 1200 | 0.4208 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.34, 'recall': 0.40476190476190477, 'f1': 0.36956521739130443, 'number': 42} | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 6} | {'precision': 0.8333333333333334, 'recall': 1.0, 'f1': 0.9090909090909091, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.4731182795698925, 'recall': 0.5116279069767442, 'f1': 0.4916201117318436, 'number': 86} | 0.5474 | 0.5977 | 0.5714 | 0.9408 |66| 0.0003 | 140.0 | 1400 | 0.4155 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.3333333333333333, 'recall': 0.40476190476190477, 'f1': 0.3655913978494623, 'number': 42} | {'precision': 0.8, 'recall': 0.6666666666666666, 'f1': 0.7272727272727272, 'number': 6} | {'precision': 0.8333333333333334, 'recall': 1.0, 'f1': 0.9090909090909091, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.46808510638297873, 'recall': 0.5116279069767442, 'f1': 0.4888888888888889, 'number': 86} | 0.5497 | 0.6034 | 0.5753 | 0.9397 |67| 0.0004 | 160.0 | 1600 | 0.4277 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.391304347826087, 'recall': 0.42857142857142855, 'f1': 0.4090909090909091, 'number': 42} | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.5287356321839081, 'recall': 0.5348837209302325, 'f1': 0.5317919075144507, 'number': 86} | 0.6045 | 0.6149 | 0.6097 | 0.9431 |68| 0.0001 | 180.0 | 1800 | 0.3870 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.27586206896551724, 'recall': 0.38095238095238093, 'f1': 0.32, 'number': 42} | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.45, 'recall': 0.5232558139534884, 'f1': 0.48387096774193555, 'number': 86} | 0.5149 | 0.5977 | 0.5532 | 0.9476 |69| 0.0001 | 200.0 | 2000 | 0.3956 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.3617021276595745, 'recall': 0.40476190476190477, 'f1': 0.3820224719101123, 'number': 42} | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.5056179775280899, 'recall': 0.5232558139534884, 'f1': 0.5142857142857142, 'number': 86} | 0.5833 | 0.6034 | 0.5932 | 0.9526 |70| 0.0001 | 220.0 | 2200 | 0.4029 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.3469387755102041, 'recall': 0.40476190476190477, 'f1': 0.3736263736263736, 'number': 42} | {'precision': 0.6, 'recall': 0.5, 'f1': 0.5454545454545454, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.5, 'recall': 0.5348837209302325, 'f1': 0.5168539325842696, 'number': 86} | 0.5699 | 0.6092 | 0.5889 | 0.9508 |71| 0.0 | 240.0 | 2400 | 0.4031 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.34, 'recall': 0.40476190476190477, 'f1': 0.36956521739130443, 'number': 42} | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.4891304347826087, 'recall': 0.5232558139534884, 'f1': 0.5056179775280899, 'number': 86} | 0.5645 | 0.6034 | 0.5833 | 0.9499 |72 73 74### Framework versions75 76- Transformers 4.41.277- Pytorch 2.3.0+cu12178- Datasets 2.20.079- Tokenizers 0.19.180