End of training
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README.md
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base_model: microsoft/layoutlm-base-uncased
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tags:
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- generated_from_trainer
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datasets:
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- funsd
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model-index:
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- name: layoutlm-funsd
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results: []
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# layoutlm-funsd
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the
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It achieves the following results on the evaluation set:
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- Loss:
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## Model description
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The following hyperparameters were used during training:
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- learning_rate: 3e-05
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- train_batch_size:
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- eval_batch_size:
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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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- num_epochs:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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| 0.8515 | 4.0 | 40 | 0.8074 | {'precision': 0.5718446601941748, 'recall': 0.7280593325092707, 'f1': 0.6405655247417075, 'number': 809} | {'precision': 0.14492753623188406, 'recall': 0.08403361344537816, 'f1': 0.10638297872340426, 'number': 119} | {'precision': 0.6351132686084142, 'recall': 0.7370892018779343, 'f1': 0.6823120382442416, 'number': 1065} | 0.5927 | 0.6944 | 0.6396 | 0.7481 |
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| 0.6885 | 5.0 | 50 | 0.7250 | {'precision': 0.6376963350785341, 'recall': 0.7527812113720643, 'f1': 0.6904761904761906, 'number': 809} | {'precision': 0.23529411764705882, 'recall': 0.13445378151260504, 'f1': 0.17112299465240638, 'number': 119} | {'precision': 0.7158081705150977, 'recall': 0.7568075117370892, 'f1': 0.735737106344135, 'number': 1065} | 0.6659 | 0.7180 | 0.6910 | 0.7705 |
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| 0.582 | 6.0 | 60 | 0.6796 | {'precision': 0.6464323748668797, 'recall': 0.7503090234857849, 'f1': 0.694508009153318, 'number': 809} | {'precision': 0.2204724409448819, 'recall': 0.23529411764705882, 'f1': 0.22764227642276422, 'number': 119} | {'precision': 0.6873015873015873, 'recall': 0.8131455399061033, 'f1': 0.7449462365591398, 'number': 1065} | 0.6453 | 0.7531 | 0.6951 | 0.7920 |
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| 0.505 | 7.0 | 70 | 0.6522 | {'precision': 0.6307385229540918, 'recall': 0.7812113720642769, 'f1': 0.6979569298729985, 'number': 809} | {'precision': 0.21367521367521367, 'recall': 0.21008403361344538, 'f1': 0.211864406779661, 'number': 119} | {'precision': 0.7270450751252087, 'recall': 0.8178403755868544, 'f1': 0.7697746354396818, 'number': 1065} | 0.6595 | 0.7667 | 0.7090 | 0.7973 |
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| 0.4537 | 8.0 | 80 | 0.6537 | {'precision': 0.6717391304347826, 'recall': 0.7639060568603214, 'f1': 0.714864083285136, 'number': 809} | {'precision': 0.272, 'recall': 0.2857142857142857, 'f1': 0.27868852459016397, 'number': 119} | {'precision': 0.7433110367892977, 'recall': 0.8347417840375587, 'f1': 0.7863777089783283, 'number': 1065} | 0.6876 | 0.7732 | 0.7279 | 0.8010 |
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| 0.3975 | 9.0 | 90 | 0.6624 | {'precision': 0.6649269311064718, 'recall': 0.7873918417799752, 'f1': 0.7209960384833051, 'number': 809} | {'precision': 0.2676056338028169, 'recall': 0.31932773109243695, 'f1': 0.2911877394636015, 'number': 119} | {'precision': 0.7576285963382737, 'recall': 0.815962441314554, 'f1': 0.7857142857142857, 'number': 1065} | 0.6871 | 0.7747 | 0.7283 | 0.8007 |
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| 0.3619 | 10.0 | 100 | 0.6649 | {'precision': 0.6825053995680346, 'recall': 0.7812113720642769, 'f1': 0.7285302593659942, 'number': 809} | {'precision': 0.3178294573643411, 'recall': 0.3445378151260504, 'f1': 0.33064516129032256, 'number': 119} | {'precision': 0.7808098591549296, 'recall': 0.8328638497652582, 'f1': 0.8059972739663789, 'number': 1065} | 0.7120 | 0.7827 | 0.7457 | 0.8012 |
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| 0.3266 | 11.0 | 110 | 0.6796 | {'precision': 0.6776246023329798, 'recall': 0.7898640296662547, 'f1': 0.7294520547945205, 'number': 809} | {'precision': 0.2740740740740741, 'recall': 0.31092436974789917, 'f1': 0.29133858267716534, 'number': 119} | {'precision': 0.7656116338751069, 'recall': 0.8403755868544601, 'f1': 0.801253357206804, 'number': 1065} | 0.6992 | 0.7883 | 0.7410 | 0.8007 |
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| 0.3091 | 12.0 | 120 | 0.6863 | {'precision': 0.6908893709327549, 'recall': 0.7873918417799752, 'f1': 0.7359907567879839, 'number': 809} | {'precision': 0.3157894736842105, 'recall': 0.35294117647058826, 'f1': 0.33333333333333337, 'number': 119} | {'precision': 0.7680278019113814, 'recall': 0.8300469483568075, 'f1': 0.7978339350180504, 'number': 1065} | 0.7085 | 0.7842 | 0.7445 | 0.8036 |
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| 0.2903 | 13.0 | 130 | 0.7025 | {'precision': 0.6961748633879782, 'recall': 0.7873918417799752, 'f1': 0.7389791183294663, 'number': 809} | {'precision': 0.29931972789115646, 'recall': 0.3697478991596639, 'f1': 0.33082706766917297, 'number': 119} | {'precision': 0.7734855136084284, 'recall': 0.8272300469483568, 'f1': 0.7994555353901996, 'number': 1065} | 0.7097 | 0.7837 | 0.7449 | 0.8029 |
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| 0.2685 | 14.0 | 140 | 0.7073 | {'precision': 0.6958424507658644, 'recall': 0.7861557478368356, 'f1': 0.7382472431804992, 'number': 809} | {'precision': 0.2896551724137931, 'recall': 0.35294117647058826, 'f1': 0.31818181818181823, 'number': 119} | {'precision': 0.7727666955767563, 'recall': 0.8366197183098592, 'f1': 0.8034265103697025, 'number': 1065} | 0.7093 | 0.7873 | 0.7463 | 0.8034 |
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| 0.2678 | 15.0 | 150 | 0.7048 | {'precision': 0.6989130434782609, 'recall': 0.7948084054388134, 'f1': 0.7437825332562175, 'number': 809} | {'precision': 0.3049645390070922, 'recall': 0.36134453781512604, 'f1': 0.3307692307692308, 'number': 119} | {'precision': 0.7769973661106233, 'recall': 0.8309859154929577, 'f1': 0.8030852994555354, 'number': 1065} | 0.7141 | 0.7883 | 0.7493 | 0.8041 |
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### Framework versions
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base_model: microsoft/layoutlm-base-uncased
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tags:
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- generated_from_trainer
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model-index:
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- name: layoutlm-funsd
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results: []
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# layoutlm-funsd
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.0586
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- Ame: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
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- Anguages: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0}
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- Ducation: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
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- Echnical skills: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
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- Escriptions: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
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- Esignation: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
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- Hone number: {'precision': 0.75, 'recall': 1.0, 'f1': 0.8571428571428571, 'number': 3}
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- Ithub: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
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- Mail: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
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- Ocation: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
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- Ork experience company: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
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- Ork experience role: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
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- Rojects: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
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- Overall Precision: 0.12
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- Overall Recall: 0.1071
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- Overall F1: 0.1132
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- Overall Accuracy: 0.1429
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## Model description
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The following hyperparameters were used during training:
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- learning_rate: 3e-05
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- train_batch_size: 2
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- eval_batch_size: 2
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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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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Ame | Anguages | Ducation | Ear of experience | Echnical skills | Escriptions | Esignation | Hone number | Ithub | Mail | Ob | Ocation | Ork experience company | Ork experience role | Rojects | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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| 3.2249 | 1.0 | 3 | 3.1464 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | 0.0909 | 0.0714 | 0.08 | 0.1071 |
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| 2.9723 | 2.0 | 6 | 3.0839 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | 0.0769 | 0.0714 | 0.0741 | 0.0714 |
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| 2.8482 | 3.0 | 9 | 3.0586 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.75, 'recall': 1.0, 'f1': 0.8571428571428571, 'number': 3}| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | 0.12 | 0.1071 | 0.1132 | 0.1429 |
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### Framework versions
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