End of training

README.md

@@ -0,0 +1,75 @@
+---
+license: mit
+base_model: SCUT-DLVCLab/lilt-roberta-en-base
+tags:
+- generated_from_trainer
+model-index:
+- name: plus_model
+  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. -->
+
+# plus_model
+
+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: 0.2227
+- Eader: {'precision': 0.6527777777777778, 'recall': 0.6308724832214765, 'f1': 0.6416382252559727, 'number': 149}
+- Nswer: {'precision': 0.7374551971326165, 'recall': 0.7481818181818182, 'f1': 0.7427797833935018, 'number': 1100}
+- Uestion: {'precision': 0.7554833468724614, 'recall': 0.7604251839738349, 'f1': 0.7579462102689487, 'number': 1223}
+- Overall Precision: 0.7415
+- Overall Recall: 0.7472
+- Overall F1: 0.7443
+- Overall Accuracy: 0.8544
+
+## 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: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 2500
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Eader                                                                                                     | Nswer                                                                                                     | Uestion                                                                                                   | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.1843        | 0.78  | 200  | 0.2061          | {'precision': 0.6296296296296297, 'recall': 0.3422818791946309, 'f1': 0.4434782608695652, 'number': 149}  | {'precision': 0.6472906403940887, 'recall': 0.5972727272727273, 'f1': 0.6212765957446809, 'number': 1100} | {'precision': 0.7054794520547946, 'recall': 0.6737530662305805, 'f1': 0.6892513592639062, 'number': 1223} | 0.6767            | 0.6197         | 0.6470     | 0.7748           |
+| 0.1356        | 1.56  | 400  | 0.1477          | {'precision': 0.5701754385964912, 'recall': 0.436241610738255, 'f1': 0.49429657794676807, 'number': 149}  | {'precision': 0.6569148936170213, 'recall': 0.6736363636363636, 'f1': 0.6651705565529622, 'number': 1100} | {'precision': 0.6554054054054054, 'recall': 0.713818479149632, 'f1': 0.6833659491193737, 'number': 1223}  | 0.6523            | 0.6792         | 0.6655     | 0.8189           |
+| 0.102         | 2.33  | 600  | 0.1666          | {'precision': 0.38974358974358975, 'recall': 0.5100671140939598, 'f1': 0.4418604651162791, 'number': 149} | {'precision': 0.6898444647758463, 'recall': 0.6854545454545454, 'f1': 0.687642498860009, 'number': 1100}  | {'precision': 0.7024661893396977, 'recall': 0.7219950940310711, 'f1': 0.7120967741935483, 'number': 1223} | 0.6731            | 0.6930         | 0.6829     | 0.8246           |
+| 0.0836        | 3.11  | 800  | 0.1592          | {'precision': 0.6307692307692307, 'recall': 0.5503355704697986, 'f1': 0.5878136200716846, 'number': 149}  | {'precision': 0.6595012897678418, 'recall': 0.6972727272727273, 'f1': 0.6778612461334512, 'number': 1100} | {'precision': 0.7373653686826843, 'recall': 0.7277187244480785, 'f1': 0.7325102880658437, 'number': 1223} | 0.6956            | 0.7035         | 0.6995     | 0.8436           |
+| 0.0657        | 3.89  | 1000 | 0.1658          | {'precision': 0.5869565217391305, 'recall': 0.5436241610738255, 'f1': 0.5644599303135888, 'number': 149}  | {'precision': 0.7464788732394366, 'recall': 0.7227272727272728, 'f1': 0.7344110854503464, 'number': 1100} | {'precision': 0.7090620031796503, 'recall': 0.7293540474243663, 'f1': 0.7190648931882304, 'number': 1223} | 0.7184            | 0.7152         | 0.7168     | 0.8457           |
+| 0.0462        | 4.67  | 1200 | 0.1855          | {'precision': 0.656, 'recall': 0.5503355704697986, 'f1': 0.5985401459854014, 'number': 149}               | {'precision': 0.6961038961038961, 'recall': 0.730909090909091, 'f1': 0.7130820399113083, 'number': 1100}  | {'precision': 0.7286392405063291, 'recall': 0.7530662305805397, 'f1': 0.7406513872135101, 'number': 1223} | 0.7103            | 0.7310         | 0.7205     | 0.8427           |
+| 0.0441        | 5.45  | 1400 | 0.1721          | {'precision': 0.6538461538461539, 'recall': 0.5704697986577181, 'f1': 0.6093189964157705, 'number': 149}  | {'precision': 0.7275179856115108, 'recall': 0.7354545454545455, 'f1': 0.7314647377938518, 'number': 1100} | {'precision': 0.7504065040650406, 'recall': 0.7547015535568274, 'f1': 0.7525479005299633, 'number': 1223} | 0.7350            | 0.7350         | 0.7350     | 0.8555           |
+| 0.0347        | 6.23  | 1600 | 0.2052          | {'precision': 0.6312056737588653, 'recall': 0.5973154362416108, 'f1': 0.6137931034482759, 'number': 149}  | {'precision': 0.715929203539823, 'recall': 0.7354545454545455, 'f1': 0.7255605381165919, 'number': 1100}  | {'precision': 0.7475728155339806, 'recall': 0.7555192150449714, 'f1': 0.7515250101667346, 'number': 1223} | 0.7268            | 0.7371         | 0.7319     | 0.8545           |
+| 0.0294        | 7.0   | 1800 | 0.2374          | {'precision': 0.6190476190476191, 'recall': 0.610738255033557, 'f1': 0.6148648648648649, 'number': 149}   | {'precision': 0.7442075996292864, 'recall': 0.73, 'f1': 0.7370353373106929, 'number': 1100}               | {'precision': 0.7593671940049959, 'recall': 0.7457072771872445, 'f1': 0.7524752475247525, 'number': 1223} | 0.7441            | 0.7306         | 0.7373     | 0.8401           |
+| 0.0239        | 7.78  | 2000 | 0.2227          | {'precision': 0.647887323943662, 'recall': 0.6174496644295302, 'f1': 0.6323024054982819, 'number': 149}   | {'precision': 0.7139061116031886, 'recall': 0.7327272727272728, 'f1': 0.7231942575145804, 'number': 1100} | {'precision': 0.7662229617304492, 'recall': 0.7530662305805397, 'f1': 0.7595876288659793, 'number': 1223} | 0.7355            | 0.7358         | 0.7357     | 0.8472           |
+| 0.0204        | 8.56  | 2200 | 0.2263          | {'precision': 0.6375838926174496, 'recall': 0.6375838926174496, 'f1': 0.6375838926174496, 'number': 149}  | {'precision': 0.7371737173717372, 'recall': 0.7445454545454545, 'f1': 0.7408412483039349, 'number': 1100} | {'precision': 0.7540453074433657, 'recall': 0.7620605069501226, 'f1': 0.7580317202114681, 'number': 1223} | 0.7396            | 0.7468         | 0.7432     | 0.8539           |
+| 0.0184        | 9.34  | 2400 | 0.2227          | {'precision': 0.6527777777777778, 'recall': 0.6308724832214765, 'f1': 0.6416382252559727, 'number': 149}  | {'precision': 0.7374551971326165, 'recall': 0.7481818181818182, 'f1': 0.7427797833935018, 'number': 1100} | {'precision': 0.7554833468724614, 'recall': 0.7604251839738349, 'f1': 0.7579462102689487, 'number': 1223} | 0.7415            | 0.7472         | 0.7443     | 0.8544           |
+
+
+### Framework versions
+
+- Transformers 4.34.0
+- Pytorch 2.1.0.dev20230810
+- Datasets 2.14.4
+- Tokenizers 0.14.1

added_tokens.json

@@ -0,0 +1,7 @@
+{
+  "</s>": 2,
+  "<mask>": 50264,
+  "<pad>": 1,
+  "<s>": 0,
+  "<unk>": 3
+}

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": "LayoutLMv3ImageProcessor",
+  "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 +1,9 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

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
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "cls_token_box": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "eos_token": "</s>",
+  "errors": "replace",
+  "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>"
+}