Utils

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. val DEFAULT_DROPOUT_PROBABILITY: Float

   

5. val EOS_WORD: String

   

6. val LOG_MIN_VALUE: Float

   

7. val RANDOM_SEED: Long

   

8. val START_TAG: String

   

9. val STOP_TAG: String

   

10. val UNK_EMBEDDING: Int

    

11. val UNK_WORD: String

    

12. val WEIGHT_DECAY: Float

    

13. final def asInstanceOf[T0]: T0

    

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14. def clone(): AnyRef

    

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15. var concatenateCount: Int

    

16. def concatenateStates(l1: Iterable[Expression], l2: Iterable[Expression]): Iterable[Expression]

    

17. def emissionScoresToArrays(expressions: Iterable[Expression]): Array[Array[Float]]

    

18. def emissionScoresToArraysAllTasks(expressions: Array[ExpressionVector]): Array[Array[Array[Float]]]

    

19. final def eq(arg0: AnyRef): Boolean

    

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20. def equals(arg0: Any): Boolean

    

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21. def expressionDropout(expression: Expression, dropoutProb: Float, doDropout: Boolean): Expression

    

22. def finalize(): Unit

    

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23. def fromIndexToChar(s2i: Map[Char, Int]): Array[Char]

    

24. def fromIndexToString(s2i: Map[String, Int]): Array[String]

    

25. final def getClass(): Class[_]

    

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26. def getModHeadPairs(labels: IndexedSeq[Label]): Option[IndexedSeq[ModifierHeadPair]]

    

27. def greedyPredict(lattice: Array[Array[Float]]): Array[Int]

    

    Runs a greedy algorithm to generate the sequence of tag ids, ignoring transition scores

28. def hashCode(): Int

    

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29. def initializeDyNet(autoBatch: Boolean = false, mem: String = ""): Unit

    

30. def initializeEmbeddings(w2v: SanitizedWordEmbeddingMap, w2i: Map[String, Int], lookupParameters: LookupParameter): Unit

    

31. final def isInstanceOf[T0]: Boolean

    

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32. def loadParameters(dynetFilename: String, modelParameters: ParameterCollection, key: String = "/all"): Unit

    

33. def mkCharacterEmbedding(word: String, c2i: Map[Char, Int], charLookupParameters: LookupParameter, charFwRnnBuilder: RnnBuilder, charBwRnnBuilder: RnnBuilder): Expression

    

34. def mkDynetFilename(baseFilename: String): String

    

35. def mkPartitionScore(emissionScoresForSeq: ExpressionVector, transitionMatrix: ExpressionVector, startTag: Int, stopTag: Int): Expression

    

    Implements the forward algorithm to compute the partition score for this lattice This code inspired by this PyTorch implementation: https://pytorch.org/tutorials/beginner/nlp/advanced_tutorial.html

36. def mkTransitionMatrix(parameters: ParameterCollection, t2i: Map[String, Int]): LookupParameter

    

    Initializes the transition matrix for a tagset of size size T[i, j] stores a transition *to* i *from* j

37. def mkWordVocab(w2v: SanitizedWordEmbeddingMap): Map[String, Int]

    

38. def mkX2iFilename(baseFilename: String): String

    

39. final def ne(arg0: AnyRef): Boolean

    

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40. def newPrintWriter(filename: String): PrintWriter

    

41. def newSource(filename: String): Source

    

42. final def notify(): Unit

    

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43. final def notifyAll(): Unit

    

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44. def pick2D(matrix: ExpressionVector, row: Int, column: Int): Expression

    

    Picks the scalar element from an expression that is a matrix

45. def printCoNLLOutput(pw: PrintWriter, words: IndexedSeq[String], golds: IndexedSeq[String], preds: IndexedSeq[String]): Unit

    

46. def printTagScores(header: String, scores: Array[Float], i2t: Array[String]): Unit

    

47. def readString2Ids(s2iFilename: String): Map[String, Int]

    

48. def save(printWriter: PrintWriter, value: String, comment: String): Unit

    

49. def save(printWriter: PrintWriter, value: Float, comment: String): Unit

    

50. def save(printWriter: PrintWriter, value: Long, comment: String): Unit

    

51. def save[T](printWriter: PrintWriter, values: Array[T], comment: String): Unit

    

52. def save[T](printWriter: PrintWriter, values: Counter[T], comment: String)(implicit ev: (T) ⇒ Ordered[T]): Unit

    

53. def save[T](printWriter: PrintWriter, values: Map[T, Int], comment: String)(implicit ev: (T) ⇒ Ordered[T]): Unit

    

54. def saveCharMap(printWriter: PrintWriter, values: Map[Char, Int], comment: String): Unit

    

55. def sentenceLossCrf(emissionScoresForSeq: ExpressionVector, transitionMatrix: ExpressionVector, golds: IndexedSeq[Int], t2i: Map[String, Int]): Expression

    

    Objective function that maximizes the CRF probability of the gold sequence of tags for a given sentence

    Objective function that maximizes the CRF probability of the gold sequence of tags for a given sentence

    emissionScoresForSeq

    emission scores for the whole sequence, and all tags

    transitionMatrix

    transition matrix between all tags

    golds

    gold sequence of tags

    returns

    the negative prob of the gold sequence (in log space)

56. def sentenceLossGreedy(emissionScoresForSeq: ExpressionVector, golds: IndexedSeq[Int]): Expression

    

    Greedy loss function, ignoring transition scores

57. def sentenceScore(emissionScoresForSeq: ExpressionVector, transitionMatrix: ExpressionVector, tagCount: Int, tagSeq: IndexedSeq[Int], startTag: Int, stopTag: Int): Expression

    

    Computes the score of the given sequence of tags (tagSeq)

58. def setRnnDropout(rnnBuilder: RnnBuilder, dropoutProb: Float, doDropout: Boolean): Unit

    

59. def srlPredict(lattice: Array[Array[Float]], predPosition: Int, oId: Int): Array[Int]

    

60. def stringToChar(string: String): Char

    

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61. def stringToCharInt(string: String): Char

    

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62. def stringToString(string: String): String

    

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63. final def synchronized[T0](arg0: ⇒ T0): T0

    

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64. def toIds[T](tags: IndexedSeq[T], t2i: Map[T, Int]): IndexedSeq[Int]

    

65. def toString(): String

    

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66. def transduce(embeddings: Iterable[Expression], builder: RnnBuilder): ExpressionVector

    

67. def transduceLastOpt(embeddings: Iterable[Expression], builder: RnnBuilder): Option[Expression]

    

68. def transitionMatrixToArrays(trans: LookupParameter, size: Int): Array[Array[Float]]

    

69. def viterbi(emissionScores: Array[Array[Float]], transitionMatrix: Array[Array[Float]], tagCount: Int, startTagIdx: Int, stopTagIdx: Int): Array[Int]

    

70. final def wait(): Unit

    

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71. final def wait(arg0: Long, arg1: Int): Unit

    

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72. final def wait(arg0: Long): Unit

    

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