End of training

README.md

@@ -1,65 +1,76 @@
----
-license: mit
-base_model: SCUT-DLVCLab/lilt-roberta-en-base
-tags:
-- generated_from_trainer
-model-index:
-- name: lilt-en-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. -->
-
-# lilt-en-funsd
-
-This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.8624
-- Answer: {'precision': 0.8333333333333334, 'recall': 0.8873929008567931, 'f1': 0.8595139300533492, 'number': 817}
-- Header: {'precision': 0.5425531914893617, 'recall': 0.42857142857142855, 'f1': 0.4788732394366197, 'number': 119}
-- Question: {'precision': 0.8791208791208791, 'recall': 0.8913649025069638, 'f1': 0.8852005532503459, 'number': 1077}
-- Overall Precision: 0.8444
-- Overall Recall: 0.8624
-- Overall F1: 0.8533
-- Overall Accuracy: 0.8198
-
-## 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: 5e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- training_steps: 250
-- mixed_precision_training: Native AMP
-
-### Training results
-
-| Training Loss | Epoch   | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.3947        | 10.5263 | 200  | 0.8624          | {'precision': 0.8333333333333334, 'recall': 0.8873929008567931, 'f1': 0.8595139300533492, 'number': 817} | {'precision': 0.5425531914893617, 'recall': 0.42857142857142855, 'f1': 0.4788732394366197, 'number': 119} | {'precision': 0.8791208791208791, 'recall': 0.8913649025069638, 'f1': 0.8852005532503459, 'number': 1077} | 0.8444            | 0.8624         | 0.8533     | 0.8198           |
-
-
-### Framework versions
-
-- Transformers 4.41.1
-- Pytorch 2.3.0+cpu
-- Datasets 2.19.1
-- Tokenizers 0.19.1
+---
+license: mit
+base_model: SCUT-DLVCLab/lilt-roberta-en-base
+tags:
+- generated_from_trainer
+model-index:
+- name: lilt-en-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. -->
+
+# lilt-en-funsd
+
+This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.9061
+- Answer: {'precision': 0.8622273249138921, 'recall': 0.9192166462668299, 'f1': 0.8898104265402843, 'number': 817}
+- Header: {'precision': 0.6585365853658537, 'recall': 0.453781512605042, 'f1': 0.5373134328358209, 'number': 119}
+- Question: {'precision': 0.8928892889288929, 'recall': 0.9210770659238626, 'f1': 0.9067641681901281, 'number': 1077}
+- Overall Precision: 0.8706
+- Overall Recall: 0.8927
+- Overall F1: 0.8815
+- Overall Accuracy: 0.7959
+
+## 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: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 2500
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch    | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4359        | 10.5263  | 200  | 1.0671          | {'precision': 0.8490338164251208, 'recall': 0.8604651162790697, 'f1': 0.8547112462006079, 'number': 817} | {'precision': 0.5862068965517241, 'recall': 0.5714285714285714, 'f1': 0.5787234042553192, 'number': 119}  | {'precision': 0.8636363636363636, 'recall': 0.9173630454967502, 'f1': 0.889689329131022, 'number': 1077}  | 0.8424            | 0.8738         | 0.8578     | 0.7904           |
+| 0.0471        | 21.0526  | 400  | 1.3296          | {'precision': 0.8695136417556346, 'recall': 0.8971848225214198, 'f1': 0.883132530120482, 'number': 817}  | {'precision': 0.5606060606060606, 'recall': 0.6218487394957983, 'f1': 0.5896414342629481, 'number': 119}  | {'precision': 0.9047619047619048, 'recall': 0.8820798514391829, 'f1': 0.8932769158439117, 'number': 1077} | 0.8677            | 0.8728         | 0.8702     | 0.8099           |
+| 0.0129        | 31.5789  | 600  | 1.5594          | {'precision': 0.8595238095238096, 'recall': 0.8837209302325582, 'f1': 0.8714544357272178, 'number': 817} | {'precision': 0.559322033898305, 'recall': 0.5546218487394958, 'f1': 0.5569620253164557, 'number': 119}   | {'precision': 0.8612068965517241, 'recall': 0.9275766016713092, 'f1': 0.8931604827894501, 'number': 1077} | 0.8437            | 0.8877         | 0.8652     | 0.7923           |
+| 0.0072        | 42.1053  | 800  | 1.5918          | {'precision': 0.8317046688382194, 'recall': 0.9375764993880049, 'f1': 0.8814729574223245, 'number': 817} | {'precision': 0.6590909090909091, 'recall': 0.48739495798319327, 'f1': 0.5603864734299517, 'number': 119} | {'precision': 0.9073900841908326, 'recall': 0.9006499535747446, 'f1': 0.9040074557315937, 'number': 1077} | 0.8633            | 0.8912         | 0.8770     | 0.8019           |
+| 0.004         | 52.6316  | 1000 | 1.5382          | {'precision': 0.8539976825028969, 'recall': 0.9020807833537332, 'f1': 0.8773809523809523, 'number': 817} | {'precision': 0.6761904761904762, 'recall': 0.5966386554621849, 'f1': 0.6339285714285715, 'number': 119}  | {'precision': 0.8953703703703704, 'recall': 0.8978644382544104, 'f1': 0.8966156699119147, 'number': 1077} | 0.8667            | 0.8818         | 0.8742     | 0.8137           |
+| 0.0022        | 63.1579  | 1200 | 1.5363          | {'precision': 0.8724672228843862, 'recall': 0.8959608323133414, 'f1': 0.8840579710144928, 'number': 817} | {'precision': 0.6153846153846154, 'recall': 0.5378151260504201, 'f1': 0.5739910313901345, 'number': 119}  | {'precision': 0.8801431127012522, 'recall': 0.9136490250696379, 'f1': 0.8965831435079726, 'number': 1077} | 0.8637            | 0.8843         | 0.8738     | 0.7988           |
+| 0.0012        | 73.6842  | 1400 | 1.8518          | {'precision': 0.8717647058823529, 'recall': 0.9069767441860465, 'f1': 0.8890221955608878, 'number': 817} | {'precision': 0.6601941747572816, 'recall': 0.5714285714285714, 'f1': 0.6126126126126127, 'number': 119}  | {'precision': 0.8940639269406393, 'recall': 0.9090064995357474, 'f1': 0.9014732965009209, 'number': 1077} | 0.8730            | 0.8882         | 0.8806     | 0.7926           |
+| 0.0015        | 84.2105  | 1600 | 1.7207          | {'precision': 0.8812121212121212, 'recall': 0.8898408812729498, 'f1': 0.8855054811205847, 'number': 817} | {'precision': 0.6236559139784946, 'recall': 0.48739495798319327, 'f1': 0.5471698113207547, 'number': 119} | {'precision': 0.9014732965009208, 'recall': 0.9090064995357474, 'f1': 0.9052242256125752, 'number': 1077} | 0.8802            | 0.8763         | 0.8783     | 0.8030           |
+| 0.0007        | 94.7368  | 1800 | 1.9117          | {'precision': 0.8469387755102041, 'recall': 0.9143206854345165, 'f1': 0.8793407886992348, 'number': 817} | {'precision': 0.6222222222222222, 'recall': 0.47058823529411764, 'f1': 0.5358851674641149, 'number': 119} | {'precision': 0.8998178506375227, 'recall': 0.9173630454967502, 'f1': 0.9085057471264367, 'number': 1077} | 0.8652            | 0.8897         | 0.8773     | 0.7942           |
+| 0.0006        | 105.2632 | 2000 | 1.9061          | {'precision': 0.8622273249138921, 'recall': 0.9192166462668299, 'f1': 0.8898104265402843, 'number': 817} | {'precision': 0.6585365853658537, 'recall': 0.453781512605042, 'f1': 0.5373134328358209, 'number': 119}   | {'precision': 0.8928892889288929, 'recall': 0.9210770659238626, 'f1': 0.9067641681901281, 'number': 1077} | 0.8706            | 0.8927         | 0.8815     | 0.7959           |
+| 0.0002        | 115.7895 | 2200 | 1.8430          | {'precision': 0.8524404086265607, 'recall': 0.9192166462668299, 'f1': 0.8845700824499411, 'number': 817} | {'precision': 0.6057692307692307, 'recall': 0.5294117647058824, 'f1': 0.5650224215246636, 'number': 119}  | {'precision': 0.8828748890860693, 'recall': 0.9238625812441968, 'f1': 0.9029038112522687, 'number': 1077} | 0.8565            | 0.8987         | 0.8771     | 0.7977           |
+| 0.0003        | 126.3158 | 2400 | 1.8246          | {'precision': 0.8732394366197183, 'recall': 0.9106487148102815, 'f1': 0.8915518274415818, 'number': 817} | {'precision': 0.6354166666666666, 'recall': 0.5126050420168067, 'f1': 0.5674418604651162, 'number': 119}  | {'precision': 0.8830823737821081, 'recall': 0.9257195914577531, 'f1': 0.9038984587488668, 'number': 1077} | 0.8676            | 0.8952         | 0.8812     | 0.7990           |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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preprocessor_config.json

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-{
-  "_valid_processor_keys": [
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-    "do_resize",
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-    "do_rescale",
-    "rescale_factor",
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+    "images",
+    "do_resize",
+    "size",
+    "resample",
+    "do_rescale",
+    "rescale_factor",
+    "do_normalize",
+    "image_mean",
+    "image_std",
+    "apply_ocr",
+    "ocr_lang",
+    "tesseract_config",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
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+  "rescale_factor": 0.00392156862745098,
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+  "tesseract_config": ""
+}

special_tokens_map.json

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-{
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-    "lstrip": false,
-    "normalized": true,
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-    "single_word": false
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-  "mask_token": {
-    "content": "<mask>",
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+}

tokenizer.json

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 {
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tokenizer_config.json

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-{
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+  "add_prefix_space": true,
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+  "errors": "replace",
+  "mask_token": "<mask>",
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+  "only_label_first_subword": true,
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+  "tokenizer_class": "LayoutLMv3Tokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}