add model

.gitignore

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+checkpoint-*/

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- x_glue
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-base-NER-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: x_glue
+      type: x_glue
+      args: ner
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.2273838630806846
+    - name: Recall
+      type: recall
+      value: 0.11185727172496743
+    - name: F1
+      type: f1
+      value: 0.14994961370507223
+    - name: Accuracy
+      type: accuracy
+      value: 0.8485324947589099
+---
+
+<!-- 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-base-NER-finetuned-ner
+
+This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the x_glue dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.4380
+- Precision: 0.2274
+- Recall: 0.1119
+- F1: 0.1499
+- Accuracy: 0.8485
+
+## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0822        | 1.0   | 878  | 1.1648          | 0.2068    | 0.1101 | 0.1437 | 0.8471   |
+| 0.0102        | 2.0   | 1756 | 1.2697          | 0.2073    | 0.1110 | 0.1445 | 0.8447   |
+| 0.0049        | 3.0   | 2634 | 1.3945          | 0.2006    | 0.1073 | 0.1399 | 0.8368   |
+| 0.0025        | 4.0   | 3512 | 1.3994          | 0.2243    | 0.1126 | 0.1499 | 0.8501   |
+| 0.0011        | 5.0   | 4390 | 1.4380          | 0.2274    | 0.1119 | 0.1499 | 0.8485   |
+
+
+### Framework versions
+
+- Transformers 4.10.2
+- Pytorch 1.9.0+cu102
+- Datasets 1.12.1
+- Tokenizers 0.10.3

config.json

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+{
+  "_name_or_path": "dslim/bert-base-NER",
+  "_num_labels": 9,
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "B-MISC",
+    "2": "I-MISC",
+    "3": "B-PER",
+    "4": "I-PER",
+    "5": "B-ORG",
+    "6": "I-ORG",
+    "7": "B-LOC",
+    "8": "I-LOC"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-LOC": 7,
+    "B-MISC": 1,
+    "B-ORG": 5,
+    "B-PER": 3,
+    "I-LOC": 8,
+    "I-MISC": 2,
+    "I-ORG": 6,
+    "I-PER": 4,
+    "O": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.10.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

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special_tokens_map.json

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tokenizer_config.json

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