End of training

README.md

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+---
+library_name: transformers
+base_model: google-bert/bert-base-chinese
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert_bilstm_mega_crf-ner-weibo
+  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. -->
+
+# bert_bilstm_mega_crf-ner-weibo
+
+This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1596
+- Precision: 0.6343
+- Recall: 0.7201
+- F1: 0.6745
+- Accuracy: 0.9670
+
+## 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: 16
+- eval_batch_size: 16
+- 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
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1831        | 1.0   | 85   | 0.1327          | 0.4580    | 0.5660 | 0.5063 | 0.9644   |
+| 0.101         | 2.0   | 170  | 0.1056          | 0.5725    | 0.7201 | 0.6379 | 0.9700   |
+| 0.0612        | 3.0   | 255  | 0.1136          | 0.5990    | 0.7233 | 0.6553 | 0.9688   |
+| 0.0503        | 4.0   | 340  | 0.1315          | 0.5879    | 0.7358 | 0.6536 | 0.9653   |
+| 0.033         | 5.0   | 425  | 0.1323          | 0.6133    | 0.7233 | 0.6638 | 0.9672   |
+| 0.019         | 6.0   | 510  | 0.1398          | 0.6198    | 0.7075 | 0.6608 | 0.9655   |
+| 0.0198        | 7.0   | 595  | 0.1442          | 0.6209    | 0.7107 | 0.6628 | 0.9681   |
+| 0.0153        | 8.0   | 680  | 0.1511          | 0.6247    | 0.7170 | 0.6676 | 0.9674   |
+| 0.0133        | 9.0   | 765  | 0.1581          | 0.6212    | 0.7013 | 0.6588 | 0.9656   |
+| 0.0084        | 10.0  | 850  | 0.1596          | 0.6343    | 0.7201 | 0.6745 | 0.9670   |
+
+
+### Framework versions
+
+- Transformers 4.46.1
+- Pytorch 1.13.1+cu116
+- Datasets 3.1.0
+- Tokenizers 0.20.1

config.json

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+{
+  "_name_or_path": "google-bert/bert-base-chinese",
+  "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-GPE.NAM",
+    "2": "I-GPE.NAM",
+    "3": "B-GPE.NOM",
+    "4": "I-GPE.NOM",
+    "5": "B-LOC.NAM",
+    "6": "I-LOC.NAM",
+    "7": "B-LOC.NOM",
+    "8": "I-LOC.NOM",
+    "9": "B-ORG.NAM",
+    "10": "I-ORG.NAM",
+    "11": "B-ORG.NOM",
+    "12": "I-ORG.NOM",
+    "13": "B-PER.NAM",
+    "14": "I-PER.NAM",
+    "15": "B-PER.NOM",
+    "16": "I-PER.NOM"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-GPE.NAM": 1,
+    "B-GPE.NOM": 3,
+    "B-LOC.NAM": 5,
+    "B-LOC.NOM": 7,
+    "B-ORG.NAM": 9,
+    "B-ORG.NOM": 11,
+    "B-PER.NAM": 13,
+    "B-PER.NOM": 15,
+    "I-GPE.NAM": 2,
+    "I-GPE.NOM": 4,
+    "I-LOC.NAM": 6,
+    "I-LOC.NOM": 8,
+    "I-ORG.NAM": 10,
+    "I-ORG.NOM": 12,
+    "I-PER.NAM": 14,
+    "I-PER.NOM": 16,
+    "O": 0
+  },
+  "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": "4.46.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:81e991192b522c613a8ff255973012056fbb3aed49bd7bc98f2ff6652abbcf54
+size 406783828

special_tokens_map.json

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

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "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]"
+}

training_args.bin

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+size 4795