<summary>View label scheme (1392 labels for 3 components)</summary>


| Component | Labels |
| --- | --- |
| **`morphologizer`** | `Case=nominative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|POS=ADP`, `Case=locative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=instrumental\|POS=ADP`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Degree=positive\|POS=ADV\|Type=general`, `Number=singular\|POS=VERB\|Person=third\|Type=main\|VForm=present`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `POS=PUNCT`, `POS=PART\|Type=modal`, `Case=locative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `POS=SCONJ\|Type=subordinating`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=accusative\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `POS=CCONJ\|Type=coordinating`, `Case=genitive\|POS=ADP`, `Case=dative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Number=plural\|POS=VERB\|Person=third\|Type=main\|VForm=present`, `Number=singular\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=present`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|POS=ADP`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Animate=no\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `POS=VERB\|Type=main\|VForm=infinitive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `POS=PART\|Type=negative`, `Case=accusative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Degree=comparative\|POS=ADV\|Type=general`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Gender=masculine\|Number=singular\|POS=VERB\|Type=main\|VForm=participle`, `Case=locative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Form=digit\|POS=ADJ\|Type=ordinal`, `Number=singular\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=present`, `Number=plural\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=present`, `Case=accusative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Gender=feminine\|Number=plural\|POS=VERB\|Type=main\|VForm=participle`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Gender=neuter\|Number=singular\|POS=VERB\|Type=main\|VForm=participle`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Degree=superlative\|POS=ADV\|Type=general`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Number=plural\|POS=VERB\|Person=first\|Type=main\|VForm=present`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Gender=feminine\|Number=plural\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Gender=masculine\|Number=singular\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Gender=masculine\|Number=plural\|POS=VERB\|Type=main\|VForm=participle`, `Form=digit\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Gender=feminine\|Number=singular\|POS=VERB\|Type=main\|VForm=participle`, `Case=accusative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Gender=neuter\|Number=singular\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=locative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Number=plural\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=aorist`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=dative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=locative\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=aorist`, `POS=X`, `Case=genitive\|Form=letter\|POS=NUM\|Type=cardinal`, `Case=genitive\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Form=letter\|POS=NUM\|Type=cardinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `POS=X\|Type=foreign`, `Number=plural\|POS=VERB\|Person=second\|Type=main\|VForm=present`, `POS=PART\|Type=interrogative`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `POS=ADV\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=VERB\|Person=first\|Type=main\|VForm=present`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=dative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Animate=yes\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Number=plural\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=present`, `POS=AUX\|Type=auxiliary\|VForm=infinitive`, `Case=locative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Gender=feminine\|Number=singular\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Animate=no\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=dative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Number=plural\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=aorist`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=dative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `POS=NOUN`, `Case=vocative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=accusative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=neuter\|POS=PRON\|Person=third\|Type=interrogative`, `Case=nominative\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Number=plural\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=present`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=dative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Number=singular\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=aorist`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=accusative\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Type=interrogative`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Animate=no\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=instrumental\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=dative\|POS=ADP`, `Case=instrumental\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Gender=neuter\|Number=plural\|POS=VERB\|Type=main\|VForm=participle`, `Case=nominative\|Form=letter\|Gender=neuter\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=dative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=accusative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Form=letter\|POS=NUM\|Type=special`, `Case=accusative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=genitive\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=genitive\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Type=interrogative`, `Case=nominative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=locative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=dative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Gender=masculine\|Number=plural\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=genitive\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=genitive\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `POS=PROPN`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=locative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=accusative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=locative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Gender=neuter\|Number=plural\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Number=plural\|POS=VERB\|Person=second\|Type=main\|VForm=imperative`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=locative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Form=digit\|POS=SYM\|Type=special`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=possessive`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=nominative\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Form=digit\|POS=NUM\|Type=special`, `Case=genitive\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=locative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=genitive\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=genitive\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=aorist`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `POS=SYM`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Case=vocative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Form=digit\|POS=NUM\|Type=multiple`, `Case=instrumental\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Animate=yes\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Number=plural\|POS=VERB\|Person=first\|Type=main\|VForm=imperative`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=plural\|POS=NUM\|Type=cardinal`, `Case=accusative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=interrogative`, `Case=accusative\|Gender=neuter\|POS=PRON\|Person=third\|Type=interrogative`, `Case=locative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=locative\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Animate=no\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=dative\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=accusative\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=PRON\|Type=indefinite`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=genitive\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=locative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=locative\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=accusative\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=locative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=VERB\|Person=third\|Type=main\|VForm=aorist`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=instrumental\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `POS=ADJ`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=instrumental\|Form=letter\|POS=NUM\|Type=cardinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=instrumental\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=dative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Number=singular\|POS=VERB\|Person=second\|Type=main\|VForm=imperative`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Form=roman\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=dative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=plural\|POS=NUM\|Type=special`, `Case=locative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=accusative\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=genitive\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Form=letter\|POS=NUM\|Type=cardinal`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=genitive\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Form=roman\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=PROPN\|Type=proper`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Number=plural\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=aorist`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|POS=SYM`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=nominative\|Gender=masculine\|POS=PRON\|Person=third\|Type=interrogative`, `Case=locative\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `POS=PART`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=genitive\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=dative\|Gender=masculine\|POS=PRON\|Person=third\|Type=interrogative`, `Case=instrumental\|Definiteness=no\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `POS=INTJ`, `Case=locative\|Gender=neuter\|POS=PRON\|Person=third\|Type=interrogative`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `POS=PART\|Type=affirmative`, `Number=singular\|POS=VERB\|Person=second\|Type=main\|VForm=present`, `Case=dative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=instrumental\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=neuter\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=neuter\|POS=NUM\|Type=special`, `Case=locative\|Form=letter\|Gender=masculine\|Number=plural\|POS=NUM\|Type=cardinal`, `Case=accusative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=locative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Number=singular\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=present`, `Case=vocative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=vocative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=vocative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=vocative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=vocative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=genitive\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Number=plural\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=imperative`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=vocative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Number=singular\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=imperfect`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=nominative\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `POS=ADV`, `Case=locative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=vocative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=vocative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=PROPN\|Type=proper`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=vocative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=vocative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=interrogative`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=vocative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=dative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Case=dative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=vocative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=vocative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive` |
| **`tagger`** | `Agcfpay`, `Agcfpdy`, `Agcfpgy`, `Agcfpiy`, `Agcfply`, `Agcfpny`, `Agcfsay`, `Agcfsdy`, `Agcfsgy`, `Agcfsiy`, `Agcfsly`, `Agcfsny`, `Agcmpay`, `Agcmpgy`, `Agcmpiy`, `Agcmply`, `Agcmpny`, `Agcmsany`, `Agcmsay`, `Agcmsayn`, `Agcmsdy`, `Agcmsgy`, `Agcmsiy`, `Agcmsly`, `Agcmsny`, `Agcnpay`, `Agcnpdy`, `Agcnpgy`, `Agcnpny`, `Agcnsay`, `Agcnsdy`, `Agcnsgy`, `Agcnsiy`, `Agcnsly`, `Agcnsny`, `Agpfpay`, `Agpfpdy`, `Agpfpgy`, `Agpfpiy`, `Agpfply`, `Agpfpny`, `Agpfsay`, `Agpfsdy`, `Agpfsgy`, `Agpfsin`, `Agpfsiy`, `Agpfsly`, `Agpfsny`, `Agpfsvy`, `Agpmpay`, `Agpmpdy`, `Agpmpgy`, `Agpmpiy`, `Agpmply`, `Agpmpny`, `Agpmpvy`, `Agpmsan`, `Agpmsann`, `Agpmsany`, `Agpmsay`, `Agpmsayn`, `Agpmsayy`, `Agpmsdy`, `Agpmsgn`, `Agpmsgy`, `Agpmsiy`, `Agpmsln`, `Agpmsly`, `Agpmsnn`, `Agpmsny`, `Agpmsvy`, `Agpnpay`, `Agpnpdy`, `Agpnpgy`, `Agpnpiy`, `Agpnply`, `Agpnpny`, `Agpnsay`, `Agpnsdy`, `Agpnsgn`, `Agpnsgy`, `Agpnsiy`, `Agpnsln`, `Agpnsly`, `Agpnsny`, `Agsfpay`, `Agsfpdy`, `Agsfpgy`, `Agsfpiy`, `Agsfply`, `Agsfpny`, `Agsfsay`, `Agsfsgy`, `Agsfsiy`, `Agsfsly`, `Agsfsny`, `Agsmpay`, `Agsmpdy`, `Agsmpgy`, `Agsmpiy`, `Agsmply`, `Agsmpny`, `Agsmpvy`, `Agsmsany`, `Agsmsay`, `Agsmsayn`, `Agsmsayy`, `Agsmsdy`, `Agsmsgy`, `Agsmsiy`, `Agsmsly`, `Agsmsny`, `Agsnpay`, `Agsnpgy`, `Agsnply`, `Agsnpny`, `Agsnsay`, `Agsnsdy`, `Agsnsiy`, `Agsnsly`, `Agsnsny`, `Appfpay`, `Appfpdy`, `Appfpgy`, `Appfpiy`, `Appfply`, `Appfpny`, `Appfsay`, `Appfsgy`, `Appfsiy`, `Appfsly`, `Appfsny`, `Appmpay`, `Appmpdy`, `Appmpgy`, `Appmpiy`, `Appmply`, `Appmpny`, `Appmsann`, `Appmsany`, `Appmsayn`, `Appmsayy`, `Appmsdy`, `Appmsgn`, `Appmsgy`, `Appmsiy`, `Appmsly`, `Appmsnn`, `Appmsny`, `Appnpay`, `Appnpdy`, `Appnpgy`, `Appnpiy`, `Appnply`, `Appnpny`, `Appnsay`, `Appnsgy`, `Appnsly`, `Appnsny`, `Aspfpay`, `Aspfpgy`, `Aspfpiy`, `Aspfply`, `Aspfpny`, `Aspfsay`, `Aspfsdy`, `Aspfsgy`, `Aspfsiy`, `Aspfsly`, `Aspfsny`, `Aspmpay`, `Aspmpgy`, `Aspmply`, `Aspmpny`, `Aspmsayn`, `Aspmsayy`, `Aspmsdy`, `Aspmsgn`, `Aspmsgy`, `Aspmsiy`, `Aspmsln`, `Aspmsly`, `Aspmsnn`, `Aspnpay`, `Aspnpgy`, `Aspnpny`, `Aspnsay`, `Aspnsdn`, `Aspnsgn`, `Aspnsgy`, `Aspnsly`, `Aspnsny`, `Cc`, `Cs`, `I`, `Mdc`, `Mdm`, `Mdo`, `Mds`, `Mlc`, `Mlc--g`, `Mlc--i`, `Mlc--l`, `Mlcf-a`, `Mlcf-d`, `Mlcf-g`, `Mlcf-n`, `Mlcfsa`, `Mlcfsd`, `Mlcfsg`, `Mlcfsi`, `Mlcfsl`, `Mlcfsn`, `Mlcm-a`, `Mlcm-g`, `Mlcm-l`, `Mlcm-n`, `Mlcmpl`, `Mlcmpn`, `Mlcmsan`, `Mlcmsay`, `Mlcmsg`, `Mlcmsi`, `Mlcmsl`, `Mlcmsn`, `Mlcn-n`, `Mlcnsa`, `Mlcnsg`, `Mlcnsl`, `Mlcnsn`, `Mlofpa`, `Mlofpd`, `Mlofpg`, `Mlofpi`, `Mlofpl`, `Mlofpn`, `Mlofsa`, `Mlofsd`, `Mlofsg`, `Mlofsi`, `Mlofsl`, `Mlofsn`, `Mlompa`, `Mlompd`, `Mlompg`, `Mlompi`, `Mlompl`, `Mlompn`, `Mlomsan`, `Mlomsay`, `Mlomsd`, `Mlomsg`, `Mlomsi`, `Mlomsl`, `Mlomsn`, `Mlomsv`, `Mlonpa`, `Mlonpg`, `Mlonpl`, `Mlonpn`, `Mlonsa`, `Mlonsd`, `Mlonsg`, `Mlonsi`, `Mlonsl`, `Mlonsn`, `Mls`, `Mlsf-a`, `Mlsf-d`, `Mlsf-g`, `Mlsf-i`, `Mlsf-l`, `Mlsf-n`, `Mlsm-a`, `Mlsm-g`, `Mlsm-l`, `Mlsm-n`, `Mlsmpn`, `Mlsn-n`, `Mrc`, `Mro`, `Ncfpa`, `Ncfpd`, `Ncfpg`, `Ncfpi`, `Ncfpl`, `Ncfpn`, `Ncfpv`, `Ncfsa`, `Ncfsd`, `Ncfsg`, `Ncfsi`, `Ncfsl`, `Ncfsn`, `Ncfsv`, `Ncmpa`, `Ncmpd`, `Ncmpg`, `Ncmpi`, `Ncmpl`, `Ncmpn`, `Ncmpv`, `Ncmsan`, `Ncmsay`, `Ncmsd`, `Ncmsg`, `Ncmsi`, `Ncmsl`, `Ncmsn`, `Ncmsv`, `Ncnpa`, `Ncnpd`, `Ncnpg`, `Ncnpi`, `Ncnpl`, `Ncnpn`, `Ncnsa`, `Ncnsd`, `Ncnsg`, `Ncnsi`, `Ncnsl`, `Ncnsn`, `Ncnsv`, `Npfpa`, `Npfpg`, `Npfpl`, `Npfpn`, `Npfsa`, `Npfsd`, `Npfsg`, `Npfsi`, `Npfsl`, `Npfsn`, `Npmpa`, `Npmpd`, `Npmpg`, `Npmpi`, `Npmpl`, `Npmpn`, `Npmsan`, `Npmsay`, `Npmsd`, `Npmsg`, `Npmsi`, `Npmsl`, `Npmsn`, `Npmsv`, `Npnpg`, `Npnpn`, `Npnsa`, `Npnsd`, `Npnsg`, `Npnsi`, `Npnsl`, `Npnsn`, `Pd-fpa`, `Pd-fpd`, `Pd-fpg`, `Pd-fpi`, `Pd-fpl`, `Pd-fpn`, `Pd-fsa`, `Pd-fsd`, `Pd-fsg`, `Pd-fsi`, `Pd-fsl`, `Pd-fsn`, `Pd-mpa`, `Pd-mpd`, `Pd-mpg`, `Pd-mpi`, `Pd-mpl`, `Pd-mpn`, `Pd-msan`, `Pd-msay`, `Pd-msd`, `Pd-msg`, `Pd-msi`, `Pd-msl`, `Pd-msn`, `Pd-npa`, `Pd-npd`, `Pd-npg`, `Pd-npi`, `Pd-npn`, `Pd-nsa`, `Pd-nsd`, `Pd-nsg`, `Pd-nsi`, `Pd-nsl`, `Pd-nsn`, `Pi-fpa`, `Pi-fpd`, `Pi-fpg`, `Pi-fpi`, `Pi-fpl`, `Pi-fpn`, `Pi-fsa`, `Pi-fsd`, `Pi-fsg`, `Pi-fsi`, `Pi-fsl`, `Pi-fsn`, `Pi-mpa`, `Pi-mpd`, `Pi-mpg`, `Pi-mpi`, `Pi-mpl`, `Pi-mpn`, `Pi-msan`, `Pi-msay`, `Pi-msd`, `Pi-msg`, `Pi-msi`, `Pi-msl`, `Pi-msn`, `Pi-npa`, `Pi-npd`, `Pi-npg`, `Pi-npi`, `Pi-npl`, `Pi-npn`, `Pi-nsa`, `Pi-nsd`, `Pi-nsg`, `Pi-nsi`, `Pi-nsl`, `Pi-nsn`, `Pi3m-a`, `Pi3m-d`, `Pi3m-g`, `Pi3m-i`, `Pi3m-n`, `Pi3n-a`, `Pi3n-d`, `Pi3n-g`, `Pi3n-i`, `Pi3n-l`, `Pi3n-n`, `Pp1-pa`, `Pp1-pd`, `Pp1-pg`, `Pp1-pi`, `Pp1-pl`, `Pp1-pn`, `Pp1-sa`, `Pp1-sd`, `Pp1-sg`, `Pp1-si`, `Pp1-sl`, `Pp1-sn`, `Pp2-pa`, `Pp2-pd`, `Pp2-pg`, `Pp2-pn`, `Pp2-sa`, `Pp2-sd`, `Pp2-sg`, `Pp2-sl`, `Pp2-sn`, `Pp2-sv`, `Pp3-pa`, `Pp3-pd`, `Pp3-pg`, `Pp3-pi`, `Pp3-pl`, `Pp3fpn`, `Pp3fsa`, `Pp3fsd`, `Pp3fsg`, `Pp3fsi`, `Pp3fsl`, `Pp3fsn`, `Pp3mpn`, `Pp3msa`, `Pp3msd`, `Pp3msg`, `Pp3msi`, `Pp3msl`, `Pp3msn`, `Pp3npn`, `Pp3nsa`, `Pp3nsi`, `Pp3nsn`, `Pq-fpa`, `Pq-fpn`, `Pq-fsa`, `Pq-fsi`, `Pq-fsl`, `Pq-fsn`, `Pq-mpn`, `Pq-msn`, `Pq-nsn`, `Pq3m-d`, `Pq3m-n`, `Pq3n-a`, `Pq3n-l`, `Pq3n-n`, `Ps1fpa`, `Ps1fpd`, `Ps1fpg`, `Ps1fpl`, `Ps1fpn`, `Ps1fsa`, `Ps1fsd`, `Ps1fsg`, `Ps1fsi`, `Ps1fsl`, `Ps1fsn`, `Ps1fsv`, `Ps1mpa`, `Ps1mpd`, `Ps1mpg`, `Ps1mpi`, `Ps1mpl`, `Ps1mpn`, `Ps1mpv`, `Ps1msan`, `Ps1msay`, `Ps1msd`, `Ps1msg`, `Ps1msi`, `Ps1msl`, `Ps1msn`, `Ps1msv`, `Ps1npd`, `Ps1npn`, `Ps1nsa`, `Ps1nsg`, `Ps1nsi`, `Ps1nsl`, `Ps1nsn`, `Ps2fpa`, `Ps2fpl`, `Ps2fpn`, `Ps2fsa`, `Ps2fsd`, `Ps2fsg`, `Ps2fsn`, `Ps2mpa`, `Ps2mpg`, `Ps2mpl`, `Ps2mpn`, `Ps2msan`, `Ps2msd`, `Ps2msg`, `Ps2msi`, `Ps2msl`, `Ps2msn`, `Ps2npn`, `Ps2nsa`, `Ps2nsg`, `Ps2nsi`, `Ps2nsl`, `Ps2nsn`, `Ps3fpa`, `Ps3fpg`, `Ps3fpl`, `Ps3fpn`, `Ps3fsa`, `Ps3fsd`, `Ps3fsg`, `Ps3fsi`, `Ps3fsl`, `Ps3fsn`, `Ps3mpa`, `Ps3mpd`, `Ps3mpg`, `Ps3mpi`, `Ps3mpl`, `Ps3mpn`, `Ps3msan`, `Ps3msay`, `Ps3msd`, `Ps3msg`, `Ps3msi`, `Ps3msl`, `Ps3msn`, `Ps3npa`, `Ps3npg`, `Ps3npl`, `Ps3npn`, `Ps3nsa`, `Ps3nsg`, `Ps3nsi`, `Ps3nsl`, `Ps3nsn`, `Px--sa`, `Px--sd`, `Px--sg`, `Px--si`, `Px--sl`, `Px-fpa`, `Px-fpg`, `Px-fpi`, `Px-fpl`, `Px-fpn`, `Px-fsa`, `Px-fsd`, `Px-fsg`, `Px-fsi`, `Px-fsl`, `Px-mpa`, `Px-mpd`, `Px-mpg`, `Px-mpi`, `Px-mpl`, `Px-msan`, `Px-msay`, `Px-msd`, `Px-msg`, `Px-msi`, `Px-msl`, `Px-npa`, `Px-npg`, `Px-npi`, `Px-npl`, `Px-nsa`, `Px-nsg`, `Px-nsi`, `Px-nsl`, `Qo`, `Qq`, `Qr`, `Qz`, `Rgc`, `Rgp`, `Rgs`, `Rr`, `Sa`, `Sd`, `Sg`, `Si`, `Sl`, `Vaa1p`, `Vaa1s`, `Vaa2p`, `Vaa2s`, `Vaa3p`, `Vaa3s`, `Vae3s`, `Vam2p`, `Van`, `Vap-pf`, `Vap-pm`, `Vap-pn`, `Vap-sf`, `Vap-sm`, `Vap-sn`, `Var1p`, `Var1s`, `Var2p`, `Var2s`, `Var3p`, `Var3s`, `Vma3s`, `Vmm1p`, `Vmm2p`, `Vmm2s`, `Vmn`, `Vmp-pf`, `Vmp-pm`, `Vmp-pn`, `Vmp-sf`, `Vmp-sm`, `Vmp-sn`, `Vmr1p`, `Vmr1s`, `Vmr2p`, `Vmr2s`, `Vmr3p`, `Vmr3s`, `X`, `Xf`, `Y`, `Z` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `goeswith`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `xcomp` |


</details>


### Accuracy


| Type | Score |
| --- | --- |
| `POS_ACC` | 97.94 |
| `MORPH_ACC` | 93.45 |
| `TAG_ACC` | 93.42 |
| `DEP_UAS` | 88.33 |
| `DEP_LAS` | 82.92 |
| `SENTS_P` | 96.67 |
| `SENTS_R` | 96.83 |
| `SENTS_F` | 96.75 |
| `TRANSFORMER_LOSS` | 3301725.19 |
| `MORPHOLOGIZER_LOSS` | 410128.51 |
| `TAGGER_LOSS` | 393243.89 |
| `PARSER_LOSS` | 3074279.42 |


model name: hr_hroberta_pipeline


model author: danielvasic


task: token-classification


tags: hr, spacy, token-classification, model-index


tags:


- spacy
- token-classification
language:
- en
model-index:
- name: en_acnl_electra_pipeline
results:
  - task:
name: POS
type: token-classification
metrics:
    - name: POS Accuracy
type: accuracy
value: 0.9769257272
  - task:
name: SENTER
type: token-classification
metrics:
    - name: SENTER Precision
type: precision
value: 0.9508884151
    - name: SENTER Recall
type: recall
value: 0.94805839
    - name: SENTER F Score
type: f_score
value: 0.9494712937
  - task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
    - name: Unlabeled Dependencies Accuracy
type: accuracy
value: 0.9577103137
  - task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
    - name: Labeled Dependencies Accuracy
type: accuracy
value: 0.9577103137


| Feature | Description |
| --- | --- |
| **Name** | `en_acnl_electra_pipeline` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.3,<3.2.0` |
| **Default Pipeline** | `transformer`, `tagger`, `parser` |
| **Components** | `transformer`, `tagger`, `parser` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | GPL |
| **Author** | Daniel Vasić() |


### Label Scheme


<details>


<summary>View label scheme (87 labels for 2 components)</summary>


| Component | Labels |
| --- | --- |
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `VERB`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `dative`, `dep`, `det`, `dobj`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nummod`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |


| Type | Score |
| --- | --- |
| `TAG_ACC` | 97.69 |
| `DEP_UAS` | 95.77 |
| `DEP_LAS` | 94.52 |
| `SENTS_P` | 95.09 |
| `SENTS_R` | 94.81 |
| `SENTS_F` | 94.95 |
| `TRANSFORMER_LOSS` | 6123357.72 |
| `TAGGER_LOSS` | 338995.26 |
| `PARSER_LOSS` | 4101825.66 |


model name: en_acnl_electra_pipeline


tags: en, spacy, token-classification, model-index


- spacy
- token-classification
language:
- hr
model-index:
- name: hr_bertic_pipeline
results:
  - task:
name: POS
type: token-classification
metrics:
    - name: POS Accuracy
type: accuracy
value: 0.9552306643
  - task:
name: SENTER
type: token-classification
metrics:
    - name: SENTER Precision
type: precision
value: 0.9536423841
    - name: SENTER Recall
type: recall
value: 0.9616026711
    - name: SENTER F Score
type: f_score
value: 0.957605985
  - task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
    - name: Unlabeled Dependencies Accuracy
type: accuracy
value: 0.9129203844
  - task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
    - name: Labeled Dependencies Accuracy
type: accuracy
value: 0.9129203844


| Feature | Description |
| --- | --- |
| **Name** | `hr_bertic_pipeline` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.3,<3.2.0` |
| **Default Pipeline** | `transformer`, `morphologizer`, `tagger`, `parser` |
| **Components** | `transformer`, `morphologizer`, `tagger`, `parser` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |


</details>


### Accuracy


| Type | Score |
| --- | --- |
| `POS_ACC` | 98.70 |
| `MORPH_ACC` | 95.55 |
| `TAG_ACC` | 95.52 |
| `DEP_UAS` | 91.29 |
| `DEP_LAS` | 86.17 |
| `SENTS_P` | 95.36 |
| `SENTS_R` | 96.16 |
| `SENTS_F` | 95.76 |
| `TRANSFORMER_LOSS` | 24668298.17 |
| `MORPHOLOGIZER_LOSS` | 362811.40 |
| `TAGGER_LOSS` | 349660.11 |
| `PARSER_LOSS` | 2088768.64 |


model name: hr_bertic_pipeline


model author: danielvasic


task: token-classification


tags:


- spacy
- token-classification
language:
- en
model-index:
- name: en_acnl_roberta_pipeline
results:
  - task:
name: POS
type: token-classification
metrics:
    - name: POS Accuracy
type: accuracy
value: 0.9805220696
  - task:
name: SENTER
type: token-classification
metrics:
    - name: SENTER Precision
type: precision
value: 0.9380376859
    - name: SENTER Recall
type: recall
value: 0.954223356
    - name: SENTER F Score
type: f_score
value: 0.946061298
  - task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
    - name: Unlabeled Dependencies Accuracy
type: accuracy
value: 0.9597776646
  - task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
    - name: Labeled Dependencies Accuracy
type: accuracy
value: 0.9597776646


| Feature | Description |
| --- | --- |
| **Name** | `en_acnl_roberta_pipeline` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.3,<3.2.0` |
| **Default Pipeline** | `transformer`, `tagger`, `parser` |
| **Components** | `transformer`, `tagger`, `parser` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |


### Label Scheme


<details>


| Type | Score |
| --- | --- |
| `TAG_ACC` | 98.05 |
| `DEP_UAS` | 95.98 |
| `DEP_LAS` | 94.83 |
| `SENTS_P` | 93.80 |
| `SENTS_R` | 95.42 |
| `SENTS_F` | 94.61 |
| `TRANSFORMER_LOSS` | 3784861.59 |
| `TAGGER_LOSS` | 698704.80 |
| `PARSER_LOSS` | 5540167.00 |


model name: en_acnl_roberta_pipeline


# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA


# Abstract


The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer models
using different design choices. We evaluate
the performance of our pretrained models
against other existing biomedical language
models in the literature. Our results show that
we achieve state-of-the-art results on several
biomedical domain tasks despite using similar
or less computational cost compared to other
models in the literature. Our findings highlight
the significant effect of design choices on
improving the performance of biomedical language
models.


# Model Description


We fine-tuned BioM-ELECTRA-Large, which was pre-trained on PubMed Abstracts, on the SQuAD2.0 dataset. Fine-tuning the biomedical language model on the SQuAD dataset helps improve the score on the BioASQ challenge. If you plan to work with BioASQ or biomedical QA tasks, it's better to use this model over BioM-ELECTRA-Large. This model (TensorFlow version ) took the lead in the BioASQ9b-Factoid challenge (Batch 5) under the name of (UDEL-LAB2). To see the full details of BioASQ9B results, please check this link http://participants-area.bioasq.org/results/9b/phaseB/ ( you need to register).


Huggingface library doesn't implement Layer-Wise decay feature, which affects the performance on SQuAD task. The reported result of BioM-ELECTRA-SQuAD in our paper is 88.3 (F1) since we use ELECTRA open-source code with TF checkpoint, which uses Layer-Wise decay.


Training Script


```python
run_qa.py --model_name_or_path sultan/BioM-ELECTRA-Large-Discriminator \
--dataset_name squad_v2 \
--do_train \
--do_eval \
--dataloader_num_workers 20 \
--preprocessing_num_workers 20 \
--version_2_with_negative \
--num_train_epochs 2 \
--learning_rate 5e-5 \
--max_seq_length 512 \
--doc_stride 128 \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 6 \
--per_device_eval_batch_size 128
--fp16 \
--fp16_opt_level O1 \
--logging_steps 50 \
--save_steps 1000 \
--overwrite_output_dir \
--output_dir out
```


Evaluation results on SQuAD2.0 Dev Dataset


```
exact = 84.33420365535248
f1 = 87.49354241889522
total = 11873
HasAns_exact = 80.43184885290148
HasAns_f1 = 86.75958656200127
HasAns_total = 5928
NoAns_exact = 88.22539949537426
NoAns_f1 = 88.22539949537426
NoAns_total = 5945
best_exact = 84.33420365535248
best_exact_thresh = 0.0
best_f1 = 87.49354241889522
best_f1_thresh = 0.0
epoch = 2.0

```


To reproduce results in Google Colab:


- Make sure you have GPU enabled.
- Clone and install required libraries through this code


!git  clone https://github.com/huggingface/transformers


!pip3 install -e transformers


!pip3 install sentencepiece


!pip3 install -r /content/transformers/examples/pytorch/question-answering/requirements.txt


- Run this python code:


```python
python /content/transformers/examples/pytorch/question-answering/run_qa.py --model_name_or_path sultan/BioM-ELECTRA-Large-SQuAD2 \
--do_eval \
--version_2_with_negative \
--per_device_eval_batch_size 8 \
--dataset_name squad_v2 \
--overwrite_output_dir \
--fp16 \
--output_dir out
```


- You don't need to download the SQuAD2 dataset. The code will download it from the HuggingFace datasets hub.
- Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints.
- We added examples to fine-tune BioM-ELECTRA-Large on SQuAD and BioASQ7B using TensorFlow and TPU here https://github.com/salrowili/BioM-Transformers/tree/main/examples .  In this example we show that we achieve 88.22 score in SQuAD2.0 since Tensor Flow code has Layer-wise decay feature.


# Acknowledgment


We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units.


# Citation


```bibtex
@inproceedings{alrowili-shanker-2021-biom,
title = "{B}io{M}-Transformers: Building Large Biomedical Language Models with {BERT}, {ALBERT} and {ELECTRA}",
author = "Alrowili, Sultan and
Shanker, Vijay",
booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bionlp-1.24",
pages = "221--227",
abstract = "The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models.",
}
```


model name: BioM-ELECTRA-Large-SQuAD2


model author: sultan


task: question-answering


tags: pytorch, electra, question-answering, transformers, autonlp_compatible, infinity_compatible


# Abstract


# Model Description


- This model is fine-tuned on the SQuAD2.0 dataset and then on the BioASQ8B-Factoid training dataset. We convert the BioASQ8B-Factoid training dataset to SQuAD1.1 format and train and evaluate our model (BioM-ELECTRA-Base-SQuAD2) on this dataset.
- You can use this model to make a prediction (inference) directly without fine-tuning it. Try to enter a PubMed abstract in the context box in this model card and try out a couple of biomedical questions within the given context and see how it performs compared to ELECTRA original model. This model should also be useful for creating a pandemic QA system (e.g., COVID-19) .
- Please note that this version (PyTorch) is different than what we used in our participation in BioASQ9B (TensorFlow with Layer-Wise Decay). We combine all five batches of the BioASQ8B testing dataset as one dev.json file.
- Below is unofficial results of our models against the original ELECTRA base and large :


| Model | Exact Match (EM) | F1 Score  |
| ---  | --- | --- |
| ELECTRA-Base-SQuAD2-BioASQ8B | 61.89 | 74.39 |
| BioM-ELECTRA-Base-SQuAD2-BioASQ8B  | 70.31 | 80.90 |
| ELECTRA-Large-SQuAD2-BioASQ8B | 67.36 | 78.90 |
| **BioM-ELECTRA-Large-SQuAD2-BioASQ8B** | **74.31** | **84.72** |


Training script


```python
python3 run_squad.py --model_type electra --model_name_or_path sultan/BioM-ELECTRA-Large-SQuAD2 \
--train_file BioASQ8B/train.json \
--predict_file BioASQ8B/dev.json \
--do_lower_case \
--do_train \
--do_eval \
--threads 20 \
--version_2_with_negative \
--num_train_epochs 3 \
--learning_rate 5e-5 \
--max_seq_length 512 \
--doc_stride 128 \
--per_gpu_train_batch_size 8 \
--gradient_accumulation_steps 2  \
--per_gpu_eval_batch_size 128   \
--logging_steps 50 \
--save_steps 5000 \
--fp16 \
--fp16_opt_level O1 \
--overwrite_output_dir \
--output_dir BioM-ELECTRA-Large-SQuAD-BioASQ \
--overwrite_cache
```


model name: BioM-ELECTRA-Large-SQuAD2-BioASQ8B


# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA


The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer models
using different design choices. We evaluate
the performance of our pretrained models
against other existing biomedical language
models in the literature. Our results show that
we achieve state-of-the-art results on several
biomedical domain tasks despite using similar
or less computational cost compared to other
models in the literature. Our findings highlight
the significant effect of design choices on
improving the performance of biomedical language
models.


This model was pre-trained on PubMed Abstracts only with biomedical domain vocabulary for 500K steps with a batch size of 1024 on TPUv3-32 unit.


Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints.


# Colab Notebook Examples


BioM-ELECTRA-LARGE on NER and ChemProt Task [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Example_of_NER_and_ChemProt_Task_on_TPU.ipynb)


BioM-ELECTRA-Large on SQuAD2.0 and BioASQ7B Factoid tasks [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Example_of_SQuAD2_0_and_BioASQ7B_tasks_with_BioM_ELECTRA_Large_on_TPU.ipynb)


BioM-ALBERT-xxlarge on SQuAD2.0 and BioASQ7B Factoid tasks [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Example_of_SQuAD2_0_and_BioASQ7B_tasks_with_BioM_ALBERT_xxlarge_on_TPU.ipynb)


Text Classification Task With HuggingFace Transformers and PyTorchXLA on Free TPU [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Fine_Tuning_Biomedical_Models_on_Text_Classification_Task_With_HuggingFace_Transformers_and_PyTorch_XLA.ipynb)


# Acknowledgment


# Citation


model name: BioM-ELECTRA-Base-Discriminator


model author: sultan


tags: pytorch, electra, pretraining, transformers, infinity_compatible


# Abstract


# Model Description


This model was pre-trained on PMC full article for further 64k steps with a batch size of 8192, where we initiate our weights from our model BioM-ALBERT-xxlarge. Thus, the total training steps for this model is 264k+64K=328K steps. The model is very large due to the number of hidden layer size (4096). In order to help researchers with limited resources to fine-tune larger models, we created an example with PyTorch XLA. PyTorch XLA (https://github.com/pytorch/xla) is a library that allows you to use PyTorch on TPU units, which is provided for free by Google Colab and Kaggle. Follow this example to work with PyTorch/XLA [Link](https://github.com/salrowili/BioM-Transformers/blob/main/examples/Fine_Tuning_Biomedical_Models_on_Text_Classification_Task_With_HuggingFace_Transformers_and_PyTorch_XLA.ipynb). In this example we achieve 80.74 micro F1 score on ChemProt task with BioM-ALBERTxxlarge . Fine-tuning takes 43 minutes for 5 epochs .


Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints. We also updated this repo with a couple of examples on how to fine-tune LMs on text classification and questions answering tasks such as ChemProt, SQuAD, and BioASQ.


We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units.


```bibtex
@inproceedings{alrowili-shanker-2021-biom,
title = "{B}io{M}-Transformers: Building Large Biomedical Language Models with {BERT}, {ALBERT} and {ELECTRA}",
author = "Alrowili, Sultan and
Shanker, Vijay",
booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bionlp-1.24",
pages = "221--227",
abstract = "The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models.",
}
```


model name: BioM-ALBERT-xxlarge-PMC


task: fill-mask


tags: pytorch, albert, fill-mask, transformers, autonlp_compatible, infinity_compatible


ArabicTransformer small model (B6-6-6 with decoder)


<b>Paper</b> : ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective (EMNLP21)


<b>Abstract</b>


Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arabic corpora, demonstrated by both AraBERT and AraELECTRA, shows an impressive result on downstream tasks. However, pre-training Transformer-based language models is computationally expensive, especially for large-scale models. Recently, Funnel Transformer has addressed the sequential redundancy inside Transformer architecture by compressing the sequence of hidden states, leading to a significant reduction in the pretraining cost. This paper empirically studies the performance and efficiency of building an Arabic language model with Funnel Transformer and ELECTRA objective. We find that our model achieves state-of-the-art results on several Arabic downstream tasks despite using less computational resources compared to other BERT-based models.


<b>Description</b>


This model was pre-trained on 44GB of Arabic corpora using [Funnel Transformer with ELECTRA objective](https://arxiv.org/abs/2006.03236). This model has more parameters (1.39x) than ELECTRA-base architecture while having similar or slightly larger inference and fine-tuning time. The model was pre-trained with significantly less resources than state-of-the-art models. We will update you with more details about the model and our accepted paper later at EMNLP21. Check our GitHub page for the latest updates and examples: https://github.com/salrowili/ArabicTransformer


```bibtex
@inproceedings{alrowili-shanker-2021-arabictransformer-efficient,
    title = "{A}rabic{T}ransformer: Efficient Large {A}rabic Language Model with Funnel Transformer and {ELECTRA} Objective",
    author = "Alrowili, Sultan  and
      Shanker, Vijay",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.108",
    pages = "1255--1261",
    abstract = "Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arabic corpora, demonstrated by both AraBERT and AraELECTRA, shows an impressive result on downstream tasks. However, pre-training Transformer-based language models is computationally expensive, especially for large-scale models. Recently, Funnel Transformer has addressed the sequential redundancy inside Transformer architecture by compressing the sequence of hidden states, leading to a significant reduction in the pre-training cost. This paper empirically studies the performance and efficiency of building an Arabic language model with Funnel Transformer and ELECTRA objective. We find that our model achieves state-of-the-art results on several Arabic downstream tasks despite using less computational resources compared to other BERT-based models.",
}
```


model name: ArabicTransformer-intermediate


model author: sultan


task: feature-extraction


tags: pytorch, funnel, feature-extraction, arxiv:2006.03236, transformers


# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA


# Abstract


The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer models
using different design choices. We evaluate
the performance of our pretrained models
against other existing biomedical language
models in the literature. Our results show that
we achieve state-of-the-art results on several
biomedical domain tasks despite using similar
or less computational cost compared to other
models in the literature. Our findings highlight
the significant effect of design choices on
improving the performance of biomedical language
models.


# Model Description


We fine-tuned BioM-ELECTRA-Base, which was pre-trained on PubMed Abstracts, on the SQuAD2.0 dataset. Fine-tuning the biomedical language model on the SQuAD dataset helps improve the score on the BioASQ challenge. If you plan to work with BioASQ or biomedical QA tasks, it's better to use this model over BioM-ELECTRA-Base.


Huggingface library doesn't implement Layer-Wise decay feature, which affects the performance on SQuAD task. The reported result of BioM-ELECTRA-Base-SQuAD in our paper is 84.4 (F1) since we use ELECTRA open-source code with TF checkpoint, which uses Layer-Wise decay. You can downoad our TensorFlow checkpoint that was fine-tuned on SQuAD2.0 and achieved 84.4 F1 score from here https://github.com/salrowili/BioM-Transformers .


Evaluation results on SQuAD2.0 Dev Dataset


```
  eval_HasAns_exact      = 79.2679
  eval_HasAns_f1         = 86.5416
  eval_HasAns_total      =    5928
  eval_NoAns_exact       = 75.8789
  eval_NoAns_f1          = 75.8789
  eval_NoAns_total       =    5945
  eval_best_exact        =  77.571
  eval_best_exact_thresh =     0.0
  eval_best_f1           = 81.2026
  eval_best_f1_thresh    =     0.0
  eval_exact             =  77.571
  eval_f1                = 81.2026
  eval_samples           =   11979
  eval_total             =   11873

```


- First make sure to install all libraries on Google Colab and make sure GPU is enabled


```python
!git  clone https://github.com/huggingface/transformers
!pip3 install -e transformers
!pip3 install sentencepiece
!pip3 install -r /content/transformers/examples/pytorch/question-answering/requirements.txt

```


- Training script


```python
python3 transformers/examples/pytorch/question-answering/run_qa.py --model_name_or_path sultan/BioM-ELECTRA-Base-Discriminator \
--dataset_name squad_v2 \
--do_train \
--do_eval \
--dataloader_num_workers 20 \
--preprocessing_num_workers 20 \
--version_2_with_negative \
--num_train_epochs 3 \
--learning_rate 4e-5 \
--max_seq_length 512 \
--doc_stride 128 \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 3 \
--per_device_eval_batch_size 128 \
--fp16 \
--fp16_opt_level O1 \
--logging_steps 50 \
--save_steps 5000 \
--overwrite_output_dir \
--output_dir out
```


- Reproduce results without training ( only eval):


```python
python transformers/examples/pytorch/question-answering/run_qa.py --model_name_or_path sultan/BioM-ELECTRA-Base-SQuAD2 \
--do_eval \
--version_2_with_negative \
--per_device_eval_batch_size 8 \
--dataset_name squad_v2 \
--overwrite_output_dir \
--fp16 \
--output_dir out
```


- You don't need to download the SQuAD2 dataset. The code will download it from the HuggingFace datasets hub.
- Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints.


# Acknowledgment


We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units.


# Citation


```bibtex
@inproceedings{alrowili-shanker-2021-biom,
title = "{B}io{M}-Transformers: Building Large Biomedical Language Models with {BERT}, {ALBERT} and {ELECTRA}",
author = "Alrowili, Sultan and
Shanker, Vijay",
booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bionlp-1.24",
pages = "221--227",
abstract = "The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models.",
}
```


model name: BioM-ELECTRA-Base-SQuAD2


model author: sultan


task: question-answering


tags: pytorch, electra, question-answering, transformers, autonlp_compatible, infinity_compatible


# Abstract


# Model Description


This model was pre-trained on PubMed Abstracts only with biomedical domain vocabulary for 264K steps with a batch size of 8192 on TPUv3-512 unit. In order to help researchers with limited resources to fine-tune larger models, we created an example with PyTorch XLA. PyTorch XLA (https://github.com/pytorch/xla) is a library that allows you to use PyTorch on TPU units, which is provided for free by Google Colab and Kaggle. Follow this example to work with PyTorch/XLA [Link](https://github.com/salrowili/BioM-Transformers/blob/main/examples/Fine_Tuning_Biomedical_Models_on_Text_Classification_Task_With_HuggingFace_Transformers_and_PyTorch_XLA.ipynb)


Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints. We also updated this repo with a couple of examples on how to fine-tune LMs on text classification and questions answering tasks such as ChemProt, SQuAD, and BioASQ.


# Colab Notebook Examples


BioM-ELECTRA-LARGE on NER and ChemProt Task [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Example_of_NER_and_ChemProt_Task_on_TPU.ipynb)


BioM-ELECTRA-Large on SQuAD2.0 and BioASQ7B Factoid tasks [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Example_of_SQuAD2_0_and_BioASQ7B_tasks_with_BioM_ELECTRA_Large_on_TPU.ipynb)


BioM-ALBERT-xxlarge on SQuAD2.0 and BioASQ7B Factoid tasks [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Example_of_SQuAD2_0_and_BioASQ7B_tasks_with_BioM_ALBERT_xxlarge_on_TPU.ipynb)


Text Classification Task With HuggingFace Transformers and PyTorchXLA on Free TPU [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Fine_Tuning_Biomedical_Models_on_Text_Classification_Task_With_HuggingFace_Transformers_and_PyTorch_XLA.ipynb)


# Acknowledgment


model name: BioM-ALBERT-xxlarge


task: fill-mask


tags: pytorch, albert, fill-mask, transformers, autonlp_compatible, infinity_compatible


# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA


The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer models
using different design choices. We evaluate
the performance of our pretrained models
against other existing biomedical language
models in the literature. Our results show that
we achieve state-of-the-art results on several
biomedical domain tasks despite using similar
or less computational cost compared to other
models in the literature. Our findings highlight
the significant effect of design choices on
improving the performance of biomedical language
models.


This model was pre-trained on PubMed Abstracts only with biomedical domain vocabulary for 434K steps with a batch size of 4096 on TPUv3-512 unit.


In order to help researchers with limited resources to fine-tune larger models, we created an example with PyTorch XLA. PyTorch XLA (https://github.com/pytorch/xla) is a library that allows you to use PyTorch on TPU units, which is provided for free by Google Colab and Kaggle. Follow this example to work with PyTorch/XLA [Link](https://github.com/salrowili/BioM-Transformers/blob/main/examples/Fine_Tuning_Biomedical_Models_on_Text_Classification_Task_With_HuggingFace_Transformers_and_PyTorch_XLA.ipynb)


# Citation


model name: BioM-ELECTRA-Large-Discriminator


model author: sultan


tags: pytorch, electra, pretraining, transformers, infinity_compatible


ArabicTransformer Large model (B8-8-8 with decoder)


<b>Paper</b> : ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective (EMNLP21)


<b>Abstract</b>


Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arabic corpora, demonstrated by both AraBERT and AraELECTRA, shows an impressive result on downstream tasks. However, pre-training Transformer-based language models is computationally expensive, especially for large-scale models. Recently, Funnel Transformer has addressed the sequential redundancy inside Transformer architecture by compressing the sequence of hidden states, leading to a significant reduction in the pretraining cost. This paper empirically studies the performance and efficiency of building an Arabic language model with Funnel Transformer and ELECTRA objective. We find that our model achieves state-of-the-art results on several Arabic downstream tasks despite using less computational resources compared to other BERT-based models.


<b>Description</b>


This model was pre-trained on 44GB of Arabic corpora using [Funnel Transformer with ELECTRA objective](https://arxiv.org/abs/2006.03236). We will update you with more details about the model and our accepted paper later at EMNLP21. Check our GitHub page for the latest updates and examples: https://github.com/salrowili/ArabicTransformer