End of training

README.md

@@ -0,0 +1,81 @@
+---
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutlm-funsd
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# layoutlm-funsd
+
+This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7271
+- Answer: {'precision': 0.7216157205240175, 'recall': 0.8170580964153276, 'f1': 0.766376811594203, 'number': 809}
+- Header: {'precision': 0.3046875, 'recall': 0.3277310924369748, 'f1': 0.31578947368421056, 'number': 119}
+- Question: {'precision': 0.7844905320108205, 'recall': 0.8169014084507042, 'f1': 0.8003679852805888, 'number': 1065}
+- Overall Precision: 0.7292
+- Overall Recall: 0.7878
+- Overall F1: 0.7574
+- Overall Accuracy: 0.8010
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                      | Header                                                                                                      | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7704        | 1.0   | 10   | 1.5853          | {'precision': 0.0226628895184136, 'recall': 0.019777503090234856, 'f1': 0.02112211221122112, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.26928471248246844, 'recall': 0.18028169014084508, 'f1': 0.2159730033745782, 'number': 1065} | 0.1466            | 0.1044         | 0.1219     | 0.3606           |
+| 1.4598        | 2.0   | 20   | 1.2597          | {'precision': 0.14809384164222875, 'recall': 0.12484548825710753, 'f1': 0.13547954393024816, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.3946969696969697, 'recall': 0.4892018779342723, 'f1': 0.4368972746331237, 'number': 1065}   | 0.3107            | 0.3121         | 0.3114     | 0.5801           |
+| 1.1343        | 3.0   | 30   | 0.9794          | {'precision': 0.45892018779342725, 'recall': 0.48331273176761436, 'f1': 0.47080072245635163, 'number': 809} | {'precision': 0.09523809523809523, 'recall': 0.01680672268907563, 'f1': 0.02857142857142857, 'number': 119} | {'precision': 0.578684429641965, 'recall': 0.6525821596244131, 'f1': 0.6134157105030891, 'number': 1065}    | 0.5246            | 0.5459         | 0.5350     | 0.7059           |
+| 0.8715        | 4.0   | 40   | 0.8169          | {'precision': 0.5791245791245792, 'recall': 0.6378244746600742, 'f1': 0.6070588235294119, 'number': 809}    | {'precision': 0.20833333333333334, 'recall': 0.08403361344537816, 'f1': 0.11976047904191618, 'number': 119} | {'precision': 0.6848816029143898, 'recall': 0.7061032863849765, 'f1': 0.6953305594082293, 'number': 1065}   | 0.6274            | 0.6412         | 0.6342     | 0.7404           |
+| 0.6755        | 5.0   | 50   | 0.7034          | {'precision': 0.6616541353383458, 'recall': 0.761433868974042, 'f1': 0.7080459770114943, 'number': 809}     | {'precision': 0.2, 'recall': 0.14285714285714285, 'f1': 0.16666666666666666, 'number': 119}                 | {'precision': 0.6862745098039216, 'recall': 0.7887323943661971, 'f1': 0.7339449541284403, 'number': 1065}   | 0.6576            | 0.7391         | 0.6960     | 0.7888           |
+| 0.563         | 6.0   | 60   | 0.6924          | {'precision': 0.6618998978549541, 'recall': 0.8009888751545118, 'f1': 0.7248322147651008, 'number': 809}    | {'precision': 0.2191780821917808, 'recall': 0.13445378151260504, 'f1': 0.16666666666666669, 'number': 119}  | {'precision': 0.7177489177489178, 'recall': 0.7784037558685446, 'f1': 0.7468468468468469, 'number': 1065}   | 0.6765            | 0.7491         | 0.7110     | 0.7869           |
+| 0.4764        | 7.0   | 70   | 0.6676          | {'precision': 0.7162011173184357, 'recall': 0.792336217552534, 'f1': 0.7523474178403756, 'number': 809}     | {'precision': 0.26126126126126126, 'recall': 0.24369747899159663, 'f1': 0.25217391304347825, 'number': 119} | {'precision': 0.7538726333907056, 'recall': 0.8225352112676056, 'f1': 0.7867085765603951, 'number': 1065}   | 0.7131            | 0.7757         | 0.7431     | 0.8032           |
+| 0.4205        | 8.0   | 80   | 0.6759          | {'precision': 0.7108953613807982, 'recall': 0.8145859085290482, 'f1': 0.7592165898617511, 'number': 809}    | {'precision': 0.2564102564102564, 'recall': 0.25210084033613445, 'f1': 0.2542372881355932, 'number': 119}   | {'precision': 0.7594501718213058, 'recall': 0.8300469483568075, 'f1': 0.7931807985643785, 'number': 1065}   | 0.7124            | 0.7893         | 0.7489     | 0.8005           |
+| 0.3675        | 9.0   | 90   | 0.6917          | {'precision': 0.7132034632034632, 'recall': 0.8145859085290482, 'f1': 0.7605308713214081, 'number': 809}    | {'precision': 0.25984251968503935, 'recall': 0.2773109243697479, 'f1': 0.2682926829268293, 'number': 119}   | {'precision': 0.7740213523131673, 'recall': 0.8169014084507042, 'f1': 0.7948835084513477, 'number': 1065}   | 0.7182            | 0.7837         | 0.7495     | 0.7982           |
+| 0.3596        | 10.0  | 100  | 0.6906          | {'precision': 0.7193932827735645, 'recall': 0.8207663782447466, 'f1': 0.766743648960739, 'number': 809}     | {'precision': 0.3, 'recall': 0.2773109243697479, 'f1': 0.28820960698689957, 'number': 119}                  | {'precision': 0.7866786678667866, 'recall': 0.8206572769953052, 'f1': 0.8033088235294117, 'number': 1065}   | 0.7327            | 0.7883         | 0.7595     | 0.8061           |
+| 0.3121        | 11.0  | 110  | 0.6999          | {'precision': 0.7300884955752213, 'recall': 0.8158220024721878, 'f1': 0.7705779334500875, 'number': 809}    | {'precision': 0.3082706766917293, 'recall': 0.3445378151260504, 'f1': 0.3253968253968254, 'number': 119}    | {'precision': 0.7694974003466204, 'recall': 0.8338028169014085, 'f1': 0.8003605227579991, 'number': 1065}   | 0.7252            | 0.7973         | 0.7596     | 0.8035           |
+| 0.2902        | 12.0  | 120  | 0.7153          | {'precision': 0.7124183006535948, 'recall': 0.8084054388133498, 'f1': 0.7573827446438912, 'number': 809}    | {'precision': 0.3185840707964602, 'recall': 0.3025210084033613, 'f1': 0.3103448275862069, 'number': 119}    | {'precision': 0.7887067395264117, 'recall': 0.8131455399061033, 'f1': 0.8007397133610726, 'number': 1065}   | 0.7309            | 0.7807         | 0.7550     | 0.8029           |
+| 0.2776        | 13.0  | 130  | 0.7184          | {'precision': 0.728587319243604, 'recall': 0.8096415327564895, 'f1': 0.7669789227166277, 'number': 809}     | {'precision': 0.3, 'recall': 0.3277310924369748, 'f1': 0.3132530120481928, 'number': 119}                   | {'precision': 0.7759226713532513, 'recall': 0.8291079812206573, 'f1': 0.8016341352700863, 'number': 1065}   | 0.7277            | 0.7913         | 0.7582     | 0.8015           |
+| 0.2604        | 14.0  | 140  | 0.7218          | {'precision': 0.7272727272727273, 'recall': 0.8108776266996292, 'f1': 0.766803039158387, 'number': 809}     | {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119} | {'precision': 0.7809439002671416, 'recall': 0.8234741784037559, 'f1': 0.8016453382084096, 'number': 1065}   | 0.7313            | 0.7893         | 0.7592     | 0.8021           |
+| 0.2644        | 15.0  | 150  | 0.7271          | {'precision': 0.7216157205240175, 'recall': 0.8170580964153276, 'f1': 0.766376811594203, 'number': 809}     | {'precision': 0.3046875, 'recall': 0.3277310924369748, 'f1': 0.31578947368421056, 'number': 119}            | {'precision': 0.7844905320108205, 'recall': 0.8169014084507042, 'f1': 0.8003679852805888, 'number': 1065}   | 0.7292            | 0.7878         | 0.7574     | 0.8010           |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

logs/events.out.tfevents.1717411881.bebcc25fea4f.10131.0

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preprocessor_config.json

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+  "_valid_processor_keys": [
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+    "apply_ocr",
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special_tokens_map.json

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tokenizer_config.json

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+}