add model

.gitignore

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+checkpoint-*/

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- nerd
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model_index:
+- name: ner_nerd
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: nerd
+      type: nerd
+      args: nerd
+    metric:
+      name: Accuracy
+      type: accuracy
+      value: 0.9389165843185125
+---
+
+<!-- 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_nerd
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the nerd dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2553
+- Precision: 0.7495
+- Recall: 0.7859
+- F1: 0.7672
+- Accuracy: 0.9389
+
+## 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: 3e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2805        | 1.0   | 8235  | 0.1950          | 0.7355    | 0.7835 | 0.7587 | 0.9376   |
+| 0.165         | 2.0   | 16470 | 0.1919          | 0.7528    | 0.7826 | 0.7674 | 0.9400   |
+| 0.1214        | 3.0   | 24705 | 0.2124          | 0.7522    | 0.7859 | 0.7687 | 0.9395   |
+| 0.0879        | 4.0   | 32940 | 0.2259          | 0.7483    | 0.7879 | 0.7675 | 0.9391   |
+| 0.0652        | 5.0   | 41175 | 0.2550          | 0.7522    | 0.7874 | 0.7694 | 0.9390   |
+
+
+### Framework versions
+
+- Transformers 4.9.1
+- Pytorch 1.9.0+cu102
+- Datasets 1.11.0
+- Tokenizers 0.10.2

config.json

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+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "I-ART",
+    "2": "I-BUILDING",
+    "3": "I-EVENT",
+    "4": "I-LOC",
+    "5": "I-ORG",
+    "6": "I-MISC",
+    "7": "I-PER",
+    "8": "I-PRODUCT"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "I-ART": 1,
+    "I-BUILDING": 2,
+    "I-EVENT": 3,
+    "I-LOC": 4,
+    "I-MISC": 6,
+    "I-ORG": 5,
+    "I-PER": 7,
+    "I-PRODUCT": 8,
+    "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,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.9.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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special_tokens_map.json

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

tokenizer_config.json

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+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "bert-base-uncased", "tokenizer_class": "BertTokenizer"}

training_args.bin

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