End of training

README.md

@@ -1,78 +1,78 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: google-bert/bert-base-chinese
-tags:
-- generated_from_trainer
-metrics:
-- precision
-- recall
-- f1
-- accuracy
-model-index:
-- name: ner_based_bert-base-chinese_withBadcase_replaceSpace
-  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. -->
-
-# ner_based_bert-base-chinese_withBadcase_replaceSpace
-
-This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0165
-- Precision: 0.9499
-- Recall: 0.9534
-- F1: 0.9516
-- Accuracy: 0.9965
-
-## 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: 2e-05
-- train_batch_size: 128
-- eval_batch_size: 128
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 20
-- training_steps: 6520
-- mixed_precision_training: Native AMP
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.16          | 1.0   | 652  | 0.0277          | 0.8882    | 0.9065 | 0.8973 | 0.9921   |
-| 0.0254        | 2.0   | 1304 | 0.0218          | 0.8999    | 0.9349 | 0.9171 | 0.9936   |
-| 0.0192        | 3.0   | 1956 | 0.0194          | 0.9209    | 0.9392 | 0.9300 | 0.9948   |
-| 0.0129        | 4.0   | 2608 | 0.0178          | 0.9268    | 0.9422 | 0.9345 | 0.9953   |
-| 0.0111        | 5.0   | 3260 | 0.0159          | 0.9311    | 0.9526 | 0.9417 | 0.9957   |
-| 0.0096        | 6.0   | 3912 | 0.0152          | 0.9459    | 0.9506 | 0.9483 | 0.9962   |
-| 0.0072        | 7.0   | 4564 | 0.0149          | 0.9462    | 0.9549 | 0.9505 | 0.9963   |
-| 0.0063        | 8.0   | 5216 | 0.0174          | 0.9380    | 0.9584 | 0.9481 | 0.9960   |
-| 0.0055        | 9.0   | 5868 | 0.0157          | 0.9543    | 0.9515 | 0.9529 | 0.9966   |
-| 0.0042        | 10.0  | 6520 | 0.0165          | 0.9499    | 0.9534 | 0.9516 | 0.9965   |
-
-
-### Framework versions
-
-- Transformers 4.51.3
-- Pytorch 2.7.0+cu126
-- Datasets 3.6.0
-- Tokenizers 0.21.1
+---
+library_name: transformers
+license: apache-2.0
+base_model: google-bert/bert-base-chinese
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: ner_based_bert-base-chinese_withBadcase_replaceSpace
+  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. -->
+
+# ner_based_bert-base-chinese_withBadcase_replaceSpace
+
+This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0138
+- Precision: 0.9505
+- Recall: 0.9655
+- F1: 0.9579
+- Accuracy: 0.9969
+
+## 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: 2e-05
+- train_batch_size: 128
+- eval_batch_size: 128
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 20
+- training_steps: 6520
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1507        | 1.0   | 652  | 0.0249          | 0.8949    | 0.9105 | 0.9026 | 0.9928   |
+| 0.0256        | 2.0   | 1304 | 0.0189          | 0.9186    | 0.9245 | 0.9215 | 0.9945   |
+| 0.0195        | 3.0   | 1956 | 0.0169          | 0.9237    | 0.9470 | 0.9352 | 0.9952   |
+| 0.0131        | 4.0   | 2608 | 0.0161          | 0.9299    | 0.9499 | 0.9398 | 0.9956   |
+| 0.0114        | 5.0   | 3260 | 0.0149          | 0.9311    | 0.9607 | 0.9457 | 0.9959   |
+| 0.01          | 6.0   | 3912 | 0.0146          | 0.9395    | 0.9600 | 0.9497 | 0.9962   |
+| 0.0072        | 7.0   | 4564 | 0.0139          | 0.9480    | 0.9562 | 0.9521 | 0.9965   |
+| 0.0065        | 8.0   | 5216 | 0.0133          | 0.9431    | 0.9655 | 0.9542 | 0.9966   |
+| 0.0059        | 9.0   | 5868 | 0.0134          | 0.9501    | 0.9640 | 0.9570 | 0.9968   |
+| 0.0042        | 10.0  | 6520 | 0.0138          | 0.9505    | 0.9655 | 0.9579 | 0.9969   |
+
+
+### Framework versions
+
+- Transformers 4.54.0
+- Pytorch 2.7.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.21.4

config.json

@@ -1,78 +1,78 @@
-{
-  "architectures": [
-    "BertForTokenClassification"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
-  "directionality": "bidi",
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "id2label": {
-    "0": "O",
-    "1": "B-phone",
-    "2": "I-phone",
-    "3": "E-phone",
-    "4": "B-name",
-    "5": "I-name",
-    "6": "E-name",
-    "7": "S-name",
-    "8": "B-area",
-    "9": "I-area",
-    "10": "E-area",
-    "11": "S-area",
-    "12": "B-sex",
-    "13": "E-sex",
-    "14": "S-sex",
-    "15": "B-wechat",
-    "16": "I-wechat",
-    "17": "E-wechat",
-    "18": "B-age",
-    "19": "I-age",
-    "20": "E-age",
-    "21": "S-age"
-  },
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "label2id": {
-    "B-age": 18,
-    "B-area": 8,
-    "B-name": 4,
-    "B-phone": 1,
-    "B-sex": 12,
-    "B-wechat": 15,
-    "E-age": 20,
-    "E-area": 10,
-    "E-name": 6,
-    "E-phone": 3,
-    "E-sex": 13,
-    "E-wechat": 17,
-    "I-age": 19,
-    "I-area": 9,
-    "I-name": 5,
-    "I-phone": 2,
-    "I-wechat": 16,
-    "O": 0,
-    "S-age": 21,
-    "S-area": 11,
-    "S-name": 7,
-    "S-sex": 14
-  },
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 0,
-  "pooler_fc_size": 768,
-  "pooler_num_attention_heads": 12,
-  "pooler_num_fc_layers": 3,
-  "pooler_size_per_head": 128,
-  "pooler_type": "first_token_transform",
-  "position_embedding_type": "absolute",
-  "torch_dtype": "float32",
-  "transformers_version": "4.51.3",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 21128
-}
+{
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "B-phone",
+    "2": "I-phone",
+    "3": "E-phone",
+    "4": "B-name",
+    "5": "I-name",
+    "6": "E-name",
+    "7": "S-name",
+    "8": "B-area",
+    "9": "I-area",
+    "10": "E-area",
+    "11": "S-area",
+    "12": "B-sex",
+    "13": "E-sex",
+    "14": "S-sex",
+    "15": "B-wechat",
+    "16": "I-wechat",
+    "17": "E-wechat",
+    "18": "B-age",
+    "19": "I-age",
+    "20": "E-age",
+    "21": "S-age"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-age": 18,
+    "B-area": 8,
+    "B-name": 4,
+    "B-phone": 1,
+    "B-sex": 12,
+    "B-wechat": 15,
+    "E-age": 20,
+    "E-area": 10,
+    "E-name": 6,
+    "E-phone": 3,
+    "E-sex": 13,
+    "E-wechat": 17,
+    "I-age": 19,
+    "I-area": 9,
+    "I-name": 5,
+    "I-phone": 2,
+    "I-wechat": 16,
+    "O": 0,
+    "S-age": 21,
+    "S-area": 11,
+    "S-name": 7,
+    "S-sex": 14
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.54.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}

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special_tokens_map.json

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-{
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-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
-}
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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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-{
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-      "lstrip": false,
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-      "rstrip": false,
-      "single_word": false,
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-  "cls_token": "[CLS]",
-  "do_lower_case": false,
-  "extra_special_tokens": {},
-  "mask_token": "[MASK]",
-  "model_max_length": 512,
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "strip_accents": null,
-  "tokenize_chinese_chars": true,
-  "tokenizer_class": "BertTokenizer",
-  "unk_token": "[UNK]"
-}
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+  "added_tokens_decoder": {
+    "0": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+}

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