bvanaken kamalkraj commited on
Commit
f381df1
1 Parent(s): 1508c62

fix_labels (#1)

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- update config with proper label to id mappings (6afccd7a0256187bd5e77c8e52d1c7b6a19fca52)
- update model inference with pipeline (1c995847e5b7780738b5aa187397d0ffc7db67d7)


Co-authored-by: Kamal Raj Kanakarajan <kamalkraj@users.noreply.huggingface.co>

Files changed (2) hide show
  1. README.md +4 -5
  2. config.json +6 -6
README.md CHANGED
@@ -26,7 +26,7 @@ The model is based on the [ClinicalBERT - Bio + Discharge Summary BERT Model](ht
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  You can load the model via the transformers library:
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  ```
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- from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert")
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  model = AutoModelForSequenceClassification.from_pretrained("bvanaken/clinical-assertion-negation-bert")
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@@ -38,11 +38,10 @@ Example input and inference:
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  ```
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  input = "The patient recovered during the night and now denies any [entity] shortness of breath [entity]."
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- tokenized_input = tokenizer(input, return_tensors="pt")
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- output = model(**tokenized_input)
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- import numpy as np
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- predicted_label = np.argmax(output.logits.detach().numpy()) ## 1 == ABSENT
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  ```
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  ### Cite
 
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  You can load the model via the transformers library:
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  ```
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
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  tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert")
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  model = AutoModelForSequenceClassification.from_pretrained("bvanaken/clinical-assertion-negation-bert")
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  ```
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  input = "The patient recovered during the night and now denies any [entity] shortness of breath [entity]."
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+ classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
 
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+ classification = classifier(input)
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+ # [{'label': 'ABSENT', 'score': 0.9842607378959656}]
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  ```
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  ### Cite
config.json CHANGED
@@ -9,16 +9,16 @@
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  "hidden_dropout_prob": 0.1,
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  "hidden_size": 768,
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  "id2label": {
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- "0": "0",
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- "1": "1",
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- "2": "2"
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  },
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  "initializer_range": 0.02,
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  "intermediate_size": 3072,
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  "label2id": {
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- "0": 0,
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- "1": 1,
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- "2": 2
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  },
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  "language": "english",
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  "layer_norm_eps": 1e-12,
 
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  "hidden_dropout_prob": 0.1,
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  "hidden_size": 768,
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  "id2label": {
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+ "0": "PRESENT",
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+ "1": "ABSENT",
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+ "2": "POSSIBLE"
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  },
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  "initializer_range": 0.02,
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  "intermediate_size": 3072,
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  "label2id": {
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+ "PRESENT": 0,
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+ "ABSENT": 1,
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+ "POSSIBLE": 2
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  },
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  "language": "english",
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  "layer_norm_eps": 1e-12,