End of training

README.md

@@ -1,7 +1,7 @@
 ---
 library_name: transformers
 license: mit
-base_model: pabloma09/layoutlm-with-funsd
+base_model: microsoft/layoutlm-base-uncased
 tags:
 - generated_from_trainer
 datasets:
@@ -16,16 +16,16 @@ should probably proofread and complete it, then remove this comment. -->
 
 # layoutlm-with-funsd
 
-This model is a fine-tuned version of [pabloma09/layoutlm-with-funsd](https://huggingface.co/pabloma09/layoutlm-with-funsd) on the funsd dataset.
+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: 0.6555
-- Eader: {'precision': 0.45454545454545453, 'recall': 0.3125, 'f1': 0.3703703703703703, 'number': 16}
-- Nswer: {'precision': 0.4520547945205479, 'recall': 0.4782608695652174, 'f1': 0.46478873239436624, 'number': 69}
-- Uestion: {'precision': 0.44047619047619047, 'recall': 0.5362318840579711, 'f1': 0.48366013071895425, 'number': 69}
-- Overall Precision: 0.4464
-- Overall Recall: 0.4870
-- Overall F1: 0.4658
-- Overall Accuracy: 0.7819
+- Loss: 0.8090
+- Eader: {'precision': 0.3333333333333333, 'recall': 0.21875, 'f1': 0.2641509433962264, 'number': 32}
+- Nswer: {'precision': 0.3763440860215054, 'recall': 0.5, 'f1': 0.4294478527607362, 'number': 70}
+- Uestion: {'precision': 0.3368421052631579, 'recall': 0.41025641025641024, 'f1': 0.3699421965317919, 'number': 78}
+- Overall Precision: 0.3541
+- Overall Recall: 0.4111
+- Overall F1: 0.3805
+- Overall Accuracy: 0.7559
 
 ## Model description
 
@@ -55,23 +55,23 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Eader                                                                                        | Nswer                                                                                                      | Uestion                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.2774        | 1.0   | 4    | 1.2327          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16}                                   | {'precision': 0.03164556962025317, 'recall': 0.07246376811594203, 'f1': 0.04405286343612334, 'number': 69} | {'precision': 0.04433497536945813, 'recall': 0.13043478260869565, 'f1': 0.0661764705882353, 'number': 69}  | 0.0383            | 0.0909         | 0.0538     | 0.4555           |
-| 1.0828        | 2.0   | 8    | 1.0961          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16}                                   | {'precision': 0.1286549707602339, 'recall': 0.3188405797101449, 'f1': 0.18333333333333332, 'number': 69}   | {'precision': 0.12138728323699421, 'recall': 0.30434782608695654, 'f1': 0.17355371900826447, 'number': 69} | 0.125             | 0.2792         | 0.1727     | 0.5891           |
-| 0.9043        | 3.0   | 12   | 1.0033          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16}                                   | {'precision': 0.17391304347826086, 'recall': 0.34782608695652173, 'f1': 0.23188405797101447, 'number': 69} | {'precision': 0.15328467153284672, 'recall': 0.30434782608695654, 'f1': 0.20388349514563106, 'number': 69} | 0.1636            | 0.2922         | 0.2098     | 0.6183           |
-| 0.8002        | 4.0   | 16   | 0.8873          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16}                                   | {'precision': 0.2204724409448819, 'recall': 0.4057971014492754, 'f1': 0.2857142857142857, 'number': 69}    | {'precision': 0.22580645161290322, 'recall': 0.4057971014492754, 'f1': 0.29015544041450775, 'number': 69}  | 0.2222            | 0.3636         | 0.2759     | 0.6336           |
-| 0.6937        | 5.0   | 20   | 0.7914          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16}                                   | {'precision': 0.308411214953271, 'recall': 0.4782608695652174, 'f1': 0.37499999999999994, 'number': 69}    | {'precision': 0.2672413793103448, 'recall': 0.4492753623188406, 'f1': 0.33513513513513515, 'number': 69}   | 0.2844            | 0.4156         | 0.3377     | 0.6866           |
-| 0.5711        | 6.0   | 24   | 0.7298          | {'precision': 0.1, 'recall': 0.0625, 'f1': 0.07692307692307693, 'number': 16}                | {'precision': 0.375, 'recall': 0.5217391304347826, 'f1': 0.43636363636363634, 'number': 69}                | {'precision': 0.30973451327433627, 'recall': 0.5072463768115942, 'f1': 0.3846153846153846, 'number': 69}   | 0.3288            | 0.4675         | 0.3861     | 0.7273           |
-| 0.4902        | 7.0   | 28   | 0.6971          | {'precision': 0.125, 'recall': 0.125, 'f1': 0.125, 'number': 16}                             | {'precision': 0.42168674698795183, 'recall': 0.5072463768115942, 'f1': 0.4605263157894737, 'number': 69}   | {'precision': 0.3684210526315789, 'recall': 0.5072463768115942, 'f1': 0.4268292682926829, 'number': 69}    | 0.3711            | 0.4675         | 0.4138     | 0.7519           |
-| 0.4757        | 8.0   | 32   | 0.6605          | {'precision': 0.11764705882352941, 'recall': 0.125, 'f1': 0.12121212121212122, 'number': 16} | {'precision': 0.43373493975903615, 'recall': 0.5217391304347826, 'f1': 0.4736842105263158, 'number': 69}   | {'precision': 0.38636363636363635, 'recall': 0.4927536231884058, 'f1': 0.4331210191082802, 'number': 69}   | 0.3830            | 0.4675         | 0.4211     | 0.7634           |
-| 0.4611        | 9.0   | 36   | 0.6649          | {'precision': 0.14285714285714285, 'recall': 0.125, 'f1': 0.13333333333333333, 'number': 16} | {'precision': 0.4533333333333333, 'recall': 0.4927536231884058, 'f1': 0.4722222222222222, 'number': 69}    | {'precision': 0.40476190476190477, 'recall': 0.4927536231884058, 'f1': 0.4444444444444444, 'number': 69}   | 0.4046            | 0.4545         | 0.4281     | 0.7704           |
-| 0.3752        | 10.0  | 40   | 0.6528          | {'precision': 0.2727272727272727, 'recall': 0.1875, 'f1': 0.2222222222222222, 'number': 16}  | {'precision': 0.4594594594594595, 'recall': 0.4927536231884058, 'f1': 0.4755244755244755, 'number': 69}    | {'precision': 0.40963855421686746, 'recall': 0.4927536231884058, 'f1': 0.44736842105263164, 'number': 69}  | 0.4226            | 0.4610         | 0.4410     | 0.7742           |
-| 0.3663        | 11.0  | 44   | 0.6418          | {'precision': 0.3076923076923077, 'recall': 0.25, 'f1': 0.27586206896551724, 'number': 16}   | {'precision': 0.4533333333333333, 'recall': 0.4927536231884058, 'f1': 0.4722222222222222, 'number': 69}    | {'precision': 0.43373493975903615, 'recall': 0.5217391304347826, 'f1': 0.4736842105263158, 'number': 69}   | 0.4327            | 0.4805         | 0.4554     | 0.7811           |
-| 0.3244        | 12.0  | 48   | 0.6540          | {'precision': 0.5, 'recall': 0.3125, 'f1': 0.38461538461538464, 'number': 16}                | {'precision': 0.4583333333333333, 'recall': 0.4782608695652174, 'f1': 0.4680851063829787, 'number': 69}    | {'precision': 0.42857142857142855, 'recall': 0.5217391304347826, 'f1': 0.47058823529411764, 'number': 69}  | 0.4458            | 0.4805         | 0.4625     | 0.7803           |
-| 0.3294        | 13.0  | 52   | 0.6585          | {'precision': 0.5, 'recall': 0.3125, 'f1': 0.38461538461538464, 'number': 16}                | {'precision': 0.4266666666666667, 'recall': 0.463768115942029, 'f1': 0.4444444444444445, 'number': 69}     | {'precision': 0.43373493975903615, 'recall': 0.5217391304347826, 'f1': 0.4736842105263158, 'number': 69}   | 0.4345            | 0.4740         | 0.4534     | 0.7803           |
-| 0.2977        | 14.0  | 56   | 0.6577          | {'precision': 0.5, 'recall': 0.3125, 'f1': 0.38461538461538464, 'number': 16}                | {'precision': 0.4383561643835616, 'recall': 0.463768115942029, 'f1': 0.45070422535211263, 'number': 69}    | {'precision': 0.42857142857142855, 'recall': 0.5217391304347826, 'f1': 0.47058823529411764, 'number': 69}  | 0.4371            | 0.4740         | 0.4548     | 0.7796           |
-| 0.2874        | 15.0  | 60   | 0.6555          | {'precision': 0.45454545454545453, 'recall': 0.3125, 'f1': 0.3703703703703703, 'number': 16} | {'precision': 0.4520547945205479, 'recall': 0.4782608695652174, 'f1': 0.46478873239436624, 'number': 69}   | {'precision': 0.44047619047619047, 'recall': 0.5362318840579711, 'f1': 0.48366013071895425, 'number': 69}  | 0.4464            | 0.4870         | 0.4658     | 0.7819           |
+| Training Loss | Epoch | Step | Validation Loss | Eader                                                                                         | Nswer                                                                                                      | Uestion                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.2801        | 1.0   | 9    | 1.0648          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}                                    | {'precision': 0.07246376811594203, 'recall': 0.21428571428571427, 'f1': 0.10830324909747292, 'number': 70} | {'precision': 0.08292682926829269, 'recall': 0.21794871794871795, 'f1': 0.12014134275618374, 'number': 78} | 0.0777            | 0.1778         | 0.1081     | 0.6088           |
+| 0.9803        | 2.0   | 18   | 0.8556          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}                                    | {'precision': 0.1875, 'recall': 0.38571428571428573, 'f1': 0.25233644859813087, 'number': 70}              | {'precision': 0.1259259259259259, 'recall': 0.21794871794871795, 'f1': 0.1596244131455399, 'number': 78}   | 0.1577            | 0.2444         | 0.1917     | 0.7064           |
+| 0.769         | 3.0   | 27   | 0.6782          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}                                    | {'precision': 0.29591836734693877, 'recall': 0.4142857142857143, 'f1': 0.34523809523809523, 'number': 70}  | {'precision': 0.3333333333333333, 'recall': 0.38461538461538464, 'f1': 0.3571428571428571, 'number': 78}   | 0.2995            | 0.3278         | 0.3130     | 0.7740           |
+| 0.6082        | 4.0   | 36   | 0.6412          | {'precision': 0.2, 'recall': 0.125, 'f1': 0.15384615384615385, 'number': 32}                  | {'precision': 0.3333333333333333, 'recall': 0.4714285714285714, 'f1': 0.3905325443786982, 'number': 70}    | {'precision': 0.367816091954023, 'recall': 0.41025641025641024, 'f1': 0.3878787878787879, 'number': 78}    | 0.3350            | 0.3833         | 0.3575     | 0.7655           |
+| 0.5047        | 5.0   | 45   | 0.7447          | {'precision': 0.42105263157894735, 'recall': 0.25, 'f1': 0.3137254901960784, 'number': 32}    | {'precision': 0.32978723404255317, 'recall': 0.44285714285714284, 'f1': 0.3780487804878049, 'number': 70}  | {'precision': 0.36363636363636365, 'recall': 0.41025641025641024, 'f1': 0.3855421686746988, 'number': 78}  | 0.3532            | 0.3944         | 0.3727     | 0.7275           |
+| 0.422         | 6.0   | 54   | 0.6465          | {'precision': 0.2857142857142857, 'recall': 0.1875, 'f1': 0.22641509433962265, 'number': 32}  | {'precision': 0.43902439024390244, 'recall': 0.5142857142857142, 'f1': 0.4736842105263158, 'number': 70}   | {'precision': 0.46153846153846156, 'recall': 0.46153846153846156, 'f1': 0.46153846153846156, 'number': 78} | 0.4309            | 0.4333         | 0.4321     | 0.7951           |
+| 0.3607        | 7.0   | 63   | 0.7246          | {'precision': 0.3684210526315789, 'recall': 0.21875, 'f1': 0.2745098039215686, 'number': 32}  | {'precision': 0.3953488372093023, 'recall': 0.4857142857142857, 'f1': 0.43589743589743585, 'number': 70}   | {'precision': 0.4069767441860465, 'recall': 0.44871794871794873, 'f1': 0.4268292682926829, 'number': 78}   | 0.3979            | 0.4222         | 0.4097     | 0.7559           |
+| 0.3106        | 8.0   | 72   | 0.7467          | {'precision': 0.3181818181818182, 'recall': 0.21875, 'f1': 0.25925925925925924, 'number': 32} | {'precision': 0.38202247191011235, 'recall': 0.4857142857142857, 'f1': 0.42767295597484273, 'number': 70}  | {'precision': 0.3333333333333333, 'recall': 0.41025641025641024, 'f1': 0.36781609195402293, 'number': 78}  | 0.3527            | 0.4056         | 0.3773     | 0.7288           |
+| 0.2649        | 9.0   | 81   | 0.7238          | {'precision': 0.3333333333333333, 'recall': 0.21875, 'f1': 0.2641509433962264, 'number': 32}  | {'precision': 0.3953488372093023, 'recall': 0.4857142857142857, 'f1': 0.43589743589743585, 'number': 70}   | {'precision': 0.3404255319148936, 'recall': 0.41025641025641024, 'f1': 0.37209302325581395, 'number': 78}  | 0.3632            | 0.4056         | 0.3832     | 0.7758           |
+| 0.239         | 10.0  | 90   | 0.8137          | {'precision': 0.30434782608695654, 'recall': 0.21875, 'f1': 0.2545454545454546, 'number': 32} | {'precision': 0.4069767441860465, 'recall': 0.5, 'f1': 0.4487179487179487, 'number': 70}                   | {'precision': 0.37209302325581395, 'recall': 0.41025641025641024, 'f1': 0.3902439024390244, 'number': 78}  | 0.3795            | 0.4111         | 0.3947     | 0.7288           |
+| 0.2141        | 11.0  | 99   | 0.7518          | {'precision': 0.25, 'recall': 0.1875, 'f1': 0.21428571428571427, 'number': 32}                | {'precision': 0.3645833333333333, 'recall': 0.5, 'f1': 0.42168674698795183, 'number': 70}                  | {'precision': 0.31958762886597936, 'recall': 0.3974358974358974, 'f1': 0.3542857142857143, 'number': 78}   | 0.3318            | 0.4            | 0.3627     | 0.7589           |
+| 0.1978        | 12.0  | 108  | 0.8165          | {'precision': 0.3333333333333333, 'recall': 0.21875, 'f1': 0.2641509433962264, 'number': 32}  | {'precision': 0.41975308641975306, 'recall': 0.4857142857142857, 'f1': 0.4503311258278146, 'number': 70}   | {'precision': 0.37209302325581395, 'recall': 0.41025641025641024, 'f1': 0.3902439024390244, 'number': 78}  | 0.3883            | 0.4056         | 0.3967     | 0.7438           |
+| 0.1807        | 13.0  | 117  | 0.7946          | {'precision': 0.3181818181818182, 'recall': 0.21875, 'f1': 0.25925925925925924, 'number': 32} | {'precision': 0.358695652173913, 'recall': 0.4714285714285714, 'f1': 0.4074074074074074, 'number': 70}     | {'precision': 0.31958762886597936, 'recall': 0.3974358974358974, 'f1': 0.3542857142857143, 'number': 78}   | 0.3365            | 0.3944         | 0.3632     | 0.7565           |
+| 0.1705        | 14.0  | 126  | 0.8007          | {'precision': 0.3684210526315789, 'recall': 0.21875, 'f1': 0.2745098039215686, 'number': 32}  | {'precision': 0.3684210526315789, 'recall': 0.5, 'f1': 0.4242424242424242, 'number': 70}                   | {'precision': 0.32989690721649484, 'recall': 0.41025641025641024, 'f1': 0.36571428571428566, 'number': 78} | 0.3507            | 0.4111         | 0.3785     | 0.7601           |
+| 0.1676        | 15.0  | 135  | 0.8090          | {'precision': 0.3333333333333333, 'recall': 0.21875, 'f1': 0.2641509433962264, 'number': 32}  | {'precision': 0.3763440860215054, 'recall': 0.5, 'f1': 0.4294478527607362, 'number': 70}                   | {'precision': 0.3368421052631579, 'recall': 0.41025641025641024, 'f1': 0.3699421965317919, 'number': 78}   | 0.3541            | 0.4111         | 0.3805     | 0.7559           |
 
 
 ### Framework versions

logs/events.out.tfevents.1741099192.DESKTOP-HA84SVN.2309656.3

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d166a7a6d00da5506b1a09319d3deb070dae7c696f131802553754c9e3a5a314
-size 15244
+oid sha256:9429a449a6d1944ffc5aedfd2b8681b4ef6f5b31f96935acc7e5019563504d27
+size 16102

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:31495bac9adbce0599f7ab1d444219c6e8904d6c60dc9e3226164d6c5ac5718c
+oid sha256:f9bb63bb8fbcf7c09decfade85afd8c76ee77c8748bcb00d27af6986fb1f349e
 size 450548984

tokenizer_config.json

@@ -55,11 +55,9 @@
   "do_lower_case": true,
   "extra_special_tokens": {},
   "mask_token": "[MASK]",
-  "max_length": 512,
   "model_max_length": 512,
   "never_split": null,
   "only_label_first_subword": true,
-  "pad_to_multiple_of": null,
   "pad_token": "[PAD]",
   "pad_token_box": [
     0,
@@ -68,8 +66,6 @@
     0
   ],
   "pad_token_label": -100,
-  "pad_token_type_id": 0,
-  "padding_side": "right",
   "processor_class": "LayoutLMv2Processor",
   "sep_token": "[SEP]",
   "sep_token_box": [
@@ -78,11 +74,8 @@
     1000,
     1000
   ],
-  "stride": 0,
   "strip_accents": null,
   "tokenize_chinese_chars": true,
   "tokenizer_class": "LayoutLMv2Tokenizer",
-  "truncation_side": "right",
-  "truncation_strategy": "longest_first",
   "unk_token": "[UNK]"
 }