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Update README.md

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@@ -69,7 +69,7 @@ though this is still an important clinical task.
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  Use the code below to get started with the model.
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- '
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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@@ -77,7 +77,7 @@ tokenizer = AutoTokenizer.from_pretrained("semaj83/scibert_finetuned_ctmatch")
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  model = AutoModelForSequenceClassification.from_pretrained("semaj83/scibert_finetuned_ctmatch")
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- '
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  ## Training Details
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@@ -98,8 +98,8 @@ you may need to use ctproc or another method to extract relevant fields and prep
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  #### Training Hyperparameters
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- `
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- max_sequence_length=512
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  batch_size=8
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  padding='max_length'
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  truncation=True
@@ -112,7 +112,6 @@ splits={"train":0.8, "val":0.1}
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  use_trainer=True
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  fp16=True
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  early_stopping=True
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-
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  `
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@@ -120,7 +119,7 @@ early_stopping=True
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  sklearn classifier table on random test split:
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- `
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  precision recall f1-score support
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@@ -132,8 +131,7 @@ sklearn classifier table on random test split:
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  macro avg 0.70 0.66 0.67 7939
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  weighted avg 0.79 0.80 0.79 7939
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-
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- `
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  ## Model Card Authors
 
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  Use the code below to get started with the model.
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+ ```
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  model = AutoModelForSequenceClassification.from_pretrained("semaj83/scibert_finetuned_ctmatch")
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+ ```
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  ## Training Details
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  #### Training Hyperparameters
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+
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+ `max_sequence_length=512
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  batch_size=8
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  padding='max_length'
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  truncation=True
 
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  use_trainer=True
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  fp16=True
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  early_stopping=True
 
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  `
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  sklearn classifier table on random test split:
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+ ```
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  precision recall f1-score support
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  macro avg 0.70 0.66 0.67 7939
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  weighted avg 0.79 0.80 0.79 7939
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+ ```
 
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  ## Model Card Authors