abstract class MedicalBertClassification extends Serializable with MedicalClassification
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abstract
def
tag(batch: Seq[Array[Int]], useTokenTypes: Boolean = true): Seq[Array[Array[Float]]]
- Definition Classes
- MedicalClassification
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abstract
def
tagSequence(batch: Seq[Array[Int]], useTokenTypes: Boolean = true): Array[Array[Float]]
- Definition Classes
- MedicalClassification
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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def
calculateSoftmax(scores: Array[Float]): Array[Float]
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- MedicalClassification
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def
clone(): AnyRef
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def
encode(sentences: Seq[(WordpieceTokenizedSentence, Int)], maxSequenceLength: Int): Seq[Array[Int]]
Encode the input sequence to indexes IDs adding padding where necessary
Encode the input sequence to indexes IDs adding padding where necessary
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- MedicalClassification
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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def
findIndexedToken(tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tokenPiece: TokenPiece): Option[IndexedToken]
- Definition Classes
- MedicalBertClassification → MedicalClassification
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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isInstanceOf[T0]: Boolean
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final
def
ne(arg0: AnyRef): Boolean
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def
notify(): Unit
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final
def
notifyAll(): Unit
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def
predict(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, tags: Map[String, Int], useTokenTypes: Boolean = true): Seq[Annotation]
- Definition Classes
- MedicalClassification
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def
predictSequence(tokenizedSentences: Seq[TokenizedSentence], sentences: Seq[Sentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, coalesceSentences: Boolean = false, tags: Map[String, Int], useTokenTypes: Boolean = true): Seq[Annotation]
- Definition Classes
- MedicalClassification
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val
sentenceEndTokenId: Int
- Definition Classes
- MedicalBertClassification → MedicalClassification
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val
sentencePadTokenId: Int
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- protected
- Definition Classes
- MedicalBertClassification → MedicalClassification
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val
sentenceStartTokenId: Int
- Definition Classes
- MedicalBertClassification → MedicalClassification
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
- Definition Classes
- MedicalBertClassification → MedicalClassification
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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def
wordAndSpanLevelAlignmentWithTokenizer(tokenLogits: Array[Array[Float]], tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tags: Map[String, Int]): Seq[Annotation]
Word-level and span-level alignment with Tokenizer https://github.com/google-research/bert#tokenization
Word-level and span-level alignment with Tokenizer https://github.com/google-research/bert#tokenization
### Input orig_tokens = ["John", "Johanson", "'s", "house"] labels = ["NNP", "NNP", "POS", "NN"]
# bert_tokens == ["[CLS]", "john", "johan", "##son", "'", "s", "house", "[SEP]"] # orig_to_tok_map == [1, 2, 4, 6]
- Definition Classes
- MedicalClassification