End of training

README.md

@@ -0,0 +1,77 @@
+---
+library_name: transformers
+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: 1.6340
+- Answer: {'precision': 0.8372352285395763, 'recall': 0.9192166462668299, 'f1': 0.8763127187864644, 'number': 817}
+- Header: {'precision': 0.5963302752293578, 'recall': 0.5462184873949579, 'f1': 0.5701754385964912, 'number': 119}
+- Question: {'precision': 0.9025974025974026, 'recall': 0.903435468895079, 'f1': 0.9030162412993039, 'number': 1077}
+- Overall Precision: 0.8584
+- Overall Recall: 0.8887
+- Overall F1: 0.8733
+- Overall Accuracy: 0.8024
+
+## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- 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 |
+|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.411         | 10.5263  | 200  | 0.9921          | {'precision': 0.8178770949720671, 'recall': 0.8959608323133414, 'f1': 0.8551401869158878, 'number': 817} | {'precision': 0.5328467153284672, 'recall': 0.6134453781512605, 'f1': 0.5703125, 'number': 119}           | {'precision': 0.8800738007380073, 'recall': 0.8857938718662952, 'f1': 0.882924571957427, 'number': 1077}  | 0.8313            | 0.8738         | 0.8520     | 0.7889           |
+| 0.0439        | 21.0526  | 400  | 1.1727          | {'precision': 0.8136511375947996, 'recall': 0.9192166462668299, 'f1': 0.8632183908045976, 'number': 817} | {'precision': 0.5728155339805825, 'recall': 0.4957983193277311, 'f1': 0.5315315315315315, 'number': 119}  | {'precision': 0.8545135845749343, 'recall': 0.9052924791086351, 'f1': 0.87917042380523, 'number': 1077}   | 0.8237            | 0.8867         | 0.8541     | 0.7946           |
+| 0.0137        | 31.5789  | 600  | 1.2732          | {'precision': 0.8581730769230769, 'recall': 0.8739290085679314, 'f1': 0.865979381443299, 'number': 817}  | {'precision': 0.5147058823529411, 'recall': 0.5882352941176471, 'f1': 0.5490196078431372, 'number': 119}  | {'precision': 0.8467400508044031, 'recall': 0.9285051067780873, 'f1': 0.8857395925597875, 'number': 1077} | 0.8302            | 0.8862         | 0.8573     | 0.7981           |
+| 0.0075        | 42.1053  | 800  | 1.4152          | {'precision': 0.8164627363737486, 'recall': 0.8984088127294981, 'f1': 0.8554778554778554, 'number': 817} | {'precision': 0.5897435897435898, 'recall': 0.5798319327731093, 'f1': 0.5847457627118645, 'number': 119}  | {'precision': 0.8922934076137419, 'recall': 0.8922934076137419, 'f1': 0.8922934076137419, 'number': 1077} | 0.8428            | 0.8763         | 0.8592     | 0.7992           |
+| 0.0047        | 52.6316  | 1000 | 1.5824          | {'precision': 0.8137254901960784, 'recall': 0.9143206854345165, 'f1': 0.8610951008645533, 'number': 817} | {'precision': 0.627906976744186, 'recall': 0.453781512605042, 'f1': 0.526829268292683, 'number': 119}     | {'precision': 0.8995348837209303, 'recall': 0.8978644382544104, 'f1': 0.8986988847583643, 'number': 1077} | 0.8504            | 0.8783         | 0.8641     | 0.7952           |
+| 0.0018        | 63.1579  | 1200 | 1.6941          | {'precision': 0.8627450980392157, 'recall': 0.9155446756425949, 'f1': 0.8883610451306414, 'number': 817} | {'precision': 0.5193798449612403, 'recall': 0.5630252100840336, 'f1': 0.5403225806451614, 'number': 119}  | {'precision': 0.9080568720379147, 'recall': 0.8895078922934077, 'f1': 0.8986866791744841, 'number': 1077} | 0.8645            | 0.8808         | 0.8725     | 0.7959           |
+| 0.0017        | 73.6842  | 1400 | 1.6340          | {'precision': 0.8372352285395763, 'recall': 0.9192166462668299, 'f1': 0.8763127187864644, 'number': 817} | {'precision': 0.5963302752293578, 'recall': 0.5462184873949579, 'f1': 0.5701754385964912, 'number': 119}  | {'precision': 0.9025974025974026, 'recall': 0.903435468895079, 'f1': 0.9030162412993039, 'number': 1077}  | 0.8584            | 0.8887         | 0.8733     | 0.8024           |
+| 0.0011        | 84.2105  | 1600 | 1.5738          | {'precision': 0.8417508417508418, 'recall': 0.9179926560587516, 'f1': 0.8782201405152226, 'number': 817} | {'precision': 0.5545454545454546, 'recall': 0.5126050420168067, 'f1': 0.5327510917030567, 'number': 119}  | {'precision': 0.8979779411764706, 'recall': 0.9071494893221913, 'f1': 0.902540415704388, 'number': 1077}  | 0.8559            | 0.8882         | 0.8718     | 0.8095           |
+| 0.0005        | 94.7368  | 1800 | 1.5766          | {'precision': 0.8384180790960452, 'recall': 0.9082007343941249, 'f1': 0.871915393654524, 'number': 817}  | {'precision': 0.5714285714285714, 'recall': 0.47058823529411764, 'f1': 0.5161290322580646, 'number': 119} | {'precision': 0.8916211293260473, 'recall': 0.9090064995357474, 'f1': 0.9002298850574713, 'number': 1077} | 0.8539            | 0.8828         | 0.8681     | 0.8108           |
+| 0.0005        | 105.2632 | 2000 | 1.7020          | {'precision': 0.8291347207009858, 'recall': 0.9265605875152999, 'f1': 0.8751445086705203, 'number': 817} | {'precision': 0.5648148148148148, 'recall': 0.5126050420168067, 'f1': 0.5374449339207047, 'number': 119}  | {'precision': 0.8938134810710988, 'recall': 0.8987929433611885, 'f1': 0.8962962962962964, 'number': 1077} | 0.8489            | 0.8872         | 0.8676     | 0.7844           |
+| 0.0002        | 115.7895 | 2200 | 1.7386          | {'precision': 0.8264192139737991, 'recall': 0.9265605875152999, 'f1': 0.8736295441431046, 'number': 817} | {'precision': 0.58, 'recall': 0.48739495798319327, 'f1': 0.5296803652968036, 'number': 119}               | {'precision': 0.891643709825528, 'recall': 0.9015784586815228, 'f1': 0.8965835641735919, 'number': 1077}  | 0.8485            | 0.8872         | 0.8674     | 0.7826           |
+| 0.0003        | 126.3158 | 2400 | 1.7405          | {'precision': 0.8422818791946308, 'recall': 0.9216646266829865, 'f1': 0.880187025131502, 'number': 817}  | {'precision': 0.5576923076923077, 'recall': 0.48739495798319327, 'f1': 0.5201793721973094, 'number': 119} | {'precision': 0.8932842686292548, 'recall': 0.9015784586815228, 'f1': 0.8974121996303143, 'number': 1077} | 0.8547            | 0.8852         | 0.8697     | 0.7796           |
+
+
+### Framework versions
+
+- Transformers 4.47.1
+- Pytorch 2.5.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.21.0

logs/events.out.tfevents.1735647893.1b74ed05452a.1748.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b5c3f65e4bc64815a0e486f8b157c9cff10dcf2f35d3c9ff84305d8adf0fc1cf
-size 14019
+oid sha256:9e6aec4330ab3e2f9de3905880599844c9b72cee8e2be08233084b544ae73eb6
+size 14373

logs/events.out.tfevents.1735649503.1b74ed05452a.1748.1

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:334b731acfcb1a64b76d27ba2b8ed78fd2c81d7f8ab02de9eea83e3b1c7d96cc
+size 592

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:67928de8c510e9903ae3eaafa14cdce516f2e46bff844cdde8fdfd8246e47ebc
+oid sha256:eb1bdf7aaaea9f071cb65a4f11d5ac206bc53d260007121bc72f72707bcf8536
 size 520727564

preprocessor_config.json

@@ -0,0 +1,26 @@
+{
+  "apply_ocr": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "LayoutLMv3FeatureExtractor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "ocr_lang": null,
+  "processor_class": "LayoutLMv3Processor",
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  },
+  "tesseract_config": ""
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,79 @@
+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "cls_token_box": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "only_label_first_subword": true,
+  "pad_token": "<pad>",
+  "pad_token_box": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "pad_token_label": -100,
+  "processor_class": "LayoutLMv3Processor",
+  "sep_token": "</s>",
+  "sep_token_box": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "tokenizer_class": "LayoutLMv3Tokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}