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HTREC 2022

SequenceTagger Example

It uses SuperPeitho-FLAIR-v2 provided in Ancient Greek BERT

shivam

What:

Example of SequenceTagger provided in SuperPeitho-FLAIR-v2 (Ancient Greek BERT)

Context:

https://discourse.aicrowd.com/t/no-way-to-load-sequencetagger/7812

Description:

There are few missing files which cause issues with the import, this example rely's on those file availability on HF Hub and create symlink to ../LM to make it work (why? well.. lots of error message and debugging :) told to do so).

In [50]:
!pip install -U transformers
!pip install -U flair

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
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Successfully installed flair-0.11.3 gdown-4.4.0
In [1]:
!git clone https://github.com/pranaydeeps/Ancient-Greek-BERT.git
%cd Ancient-Greek-BERT
# In case git lfs starts failing, because GitHub repository have bandwidth limit
# You can manually download from google drive link hosted by me as an alternative, unzip and save as final-model.pt
!git lfs pull --include "final-model.pt"

/content/Ancient-Greek-BERT
In [23]:
!mkdir ../LM/
!git clone https://huggingface.co/pranaydeeps/Ancient-Greek-BERT ../LM/SuperPeitho-v1

Cloning into '../LM/SuperPeitho-v1'...
remote: Enumerating objects: 37, done.
remote: Total 37 (delta 0), reused 0 (delta 0), pack-reused 37
Unpacking objects: 100% (37/37), done.
In [3]:
from flair.models import SequenceTagger
tagger = SequenceTagger.load("SuperPeitho-FLAIR-v2/final-model.pt")

2022-06-13 09:11:15,351 loading file SuperPeitho-FLAIR-v2/final-model.pt
2022-06-13 09:11:19,061 SequenceTagger predicts: Dictionary with 1030 tags: <unk>, O, a-p---na-, v2spma---, u--------, d--------, v-papamn-, r--------, l-s---ma-, n-s---ma-, v3siie---, l-s---nn-, l-s---fg-, n-s---fg-, l-s---mg-, n-s---mg-, v3ppia---, i--------, n-s---mn-, v3saia---, p-p---fd-, v-sppamn-, a-s---mn-, n-p---mg-, c--------, v3saoa---, p-s---mn-, l-s---mn-, v3siia---, v-sapamg-, b--------, p-s---cg-, p-s---fd-, l-p---mg-, a-p---mg-, a-s---ma-, v-sppamg-, v3spia---, a-p---ng-, n-p---ng-, _, v3piie---, l-p---md-, a-p---md-, v-pppamn-, p-p---ma-, l-s---fa-, n-s---fa-, n-p---na-, v3paia---
In [7]:
from flair.data import Sentence

sentence = Sentence("εενομεναα")
tagger.predict(sentence)
print(sentence)
sentence = Sentence("ἐγγινομένα πάθη μὴ σβεννύντες ἀλλὰ τῆ εκλύσει")
tagger.predict(sentence)
print(sentence)

Sentence: "εενομεναα" → ["εενομεναα"/v-prpena-]
Sentence: "ἐγγινομένα πάθη μὴ σβεννύντες ἀλλὰ τῆ εκλύσει" → ["ἐγγινομένα"/v-prpena-, "πάθη"/n-p---na-, "μὴ"/d--------, "σβεννύντες"/v-pappmn-, "ἀλλὰ"/b--------, "τῆ"/l-s---fd-, "εκλύσει"/n-s---fd-]

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