End of training

README.md

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+---
+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: 1.6973
+- Answer: {'precision': 0.8658109684947491, 'recall': 0.9082007343941249, 'f1': 0.886499402628435, 'number': 817}
+- Header: {'precision': 0.6770833333333334, 'recall': 0.5462184873949579, 'f1': 0.6046511627906976, 'number': 119}
+- Question: {'precision': 0.9074243813015582, 'recall': 0.9192200557103064, 'f1': 0.9132841328413284, 'number': 1077}
+- Overall Precision: 0.8792
+- Overall Recall: 0.8927
+- Overall F1: 0.8859
+- Overall Accuracy: 0.8011
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.1857        | 26.32  | 500  | 1.4181          | {'precision': 0.8298109010011123, 'recall': 0.9130966952264382, 'f1': 0.8694638694638694, 'number': 817} | {'precision': 0.6923076923076923, 'recall': 0.5294117647058824, 'f1': 0.5999999999999999, 'number': 119} | {'precision': 0.886672710788758, 'recall': 0.9080779944289693, 'f1': 0.8972477064220182, 'number': 1077}  | 0.8538            | 0.8877         | 0.8704     | 0.7981           |
+| 0.0068        | 52.63  | 1000 | 1.6084          | {'precision': 0.8581235697940504, 'recall': 0.9179926560587516, 'f1': 0.8870490833826139, 'number': 817} | {'precision': 0.5877192982456141, 'recall': 0.5630252100840336, 'f1': 0.5751072961373391, 'number': 119} | {'precision': 0.9083255378858747, 'recall': 0.9015784586815228, 'f1': 0.9049394221808015, 'number': 1077} | 0.8692            | 0.8882         | 0.8786     | 0.7956           |
+| 0.0018        | 78.95  | 1500 | 1.6068          | {'precision': 0.8742655699177438, 'recall': 0.9106487148102815, 'f1': 0.8920863309352519, 'number': 817} | {'precision': 0.6050420168067226, 'recall': 0.6050420168067226, 'f1': 0.6050420168067226, 'number': 119} | {'precision': 0.902867715078631, 'recall': 0.9062209842154132, 'f1': 0.9045412418906396, 'number': 1077}  | 0.8737            | 0.8902         | 0.8819     | 0.8095           |
+| 0.0007        | 105.26 | 2000 | 1.6522          | {'precision': 0.8611111111111112, 'recall': 0.9106487148102815, 'f1': 0.8851873884592504, 'number': 817} | {'precision': 0.6126126126126126, 'recall': 0.5714285714285714, 'f1': 0.591304347826087, 'number': 119}  | {'precision': 0.9098513011152416, 'recall': 0.9090064995357474, 'f1': 0.9094287041337669, 'number': 1077} | 0.8732            | 0.8897         | 0.8814     | 0.8028           |
+| 0.0002        | 131.58 | 2500 | 1.6973          | {'precision': 0.8658109684947491, 'recall': 0.9082007343941249, 'f1': 0.886499402628435, 'number': 817}  | {'precision': 0.6770833333333334, 'recall': 0.5462184873949579, 'f1': 0.6046511627906976, 'number': 119} | {'precision': 0.9074243813015582, 'recall': 0.9192200557103064, 'f1': 0.9132841328413284, 'number': 1077} | 0.8792            | 0.8927         | 0.8859     | 0.8011           |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.0
+- Tokenizers 0.15.0

config.json

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+{
+  "_name_or_path": "SCUT-DLVCLab/lilt-roberta-en-base",
+  "architectures": [
+    "LiltForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "channel_shrink_ratio": 4,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "B-HEADER",
+    "2": "I-HEADER",
+    "3": "B-QUESTION",
+    "4": "I-QUESTION",
+    "5": "B-ANSWER",
+    "6": "I-ANSWER"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-ANSWER": 5,
+    "B-HEADER": 1,
+    "B-QUESTION": 3,
+    "I-ANSWER": 6,
+    "I-HEADER": 2,
+    "I-QUESTION": 4,
+    "O": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "max_2d_position_embeddings": 1024,
+  "max_position_embeddings": 514,
+  "model_type": "lilt",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

model.safetensors

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preprocessor_config.json

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

runs/Dec24_08-39-13_5a294c623c7c/events.out.tfevents.1703407164.5a294c623c7c.1939.0

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
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+  "eos_token": {
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+    "lstrip": false,
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+  "mask_token": {
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+  "pad_token": {
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+    "rstrip": false,
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+  "sep_token": {
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
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+  "errors": "replace",
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+  "model_max_length": 512,
+  "only_label_first_subword": true,
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+}

training_args.bin

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