Update README.md

README.md

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+---
+metrics:
+- accuracy
+- precision
+pipeline_tag: token-classification
+---
 tokenclass-wnut
 
 This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
 
 Loss: 0.2858
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 Precision: 0.4846
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 Recall: 0.2632
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 F1: 0.3411
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 Accuracy: 0.9386
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 Model description
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 More information needed
 
 Intended uses & limitations
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 More information needed
 
 Training and evaluation data
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 More information needed
 
 Training procedure
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 Training hyperparameters
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 The following hyperparameters were used during training:
 
 learning_rate: 2e-05
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 train_batch_size: 16
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 eval_batch_size: 16
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 seed: 42
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 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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 lr_scheduler_type: linear
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 num_epochs: 2
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 Training results
-Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
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+Training  Loss	 Epoch	 Step	 Validation  Loss	Precision	Recall	F1	Accuracy
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 No log	1.0	213	0.2976	0.3873	0.1974	0.2615	0.9352
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 No log	2.0	426	0.2858	0.4846	0.2632	0.3411	0.9386
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 Framework versions
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 Transformers 4.20.1
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 Pytorch 1.11.0+cpu
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 Datasets 2.1.0
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 Tokenizers 0.12.1