hankcs--hanlp
wehub-resource-sync
73 行
3.7 KiB
Python
73 行
3.7 KiB
Python
# -*- coding:utf-8 -*-
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# Author: hankcs
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# Date: 2022-01-25 11:47
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from hanlp_common.constant import HANLP_URL
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AMR3_SEQ2SEQ_BART_LARGE = HANLP_URL + 'amr/amr3_seq2seq_bart_large_83.30_20220125_114450.zip'
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'''A seq2seq (:cite:`bevilacqua-etal-2021-one`) BART (:cite:`lewis-etal-2020-bart`) large parser trained on Abstract
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Meaning Representation 3.0 (:cite:`knight2014abstract`). Its performance is
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=================== ========= ========= =========
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Metric P R F1
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=================== ========= ========= =========
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Smatch 84.00 82.60 83.30
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Unlabeled 86.40 84.90 85.70
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No WSD 84.50 83.10 83.80
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Non_sense_frames 91.90 91.30 91.60
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Wikification 81.70 80.80 81.20
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Named Ent. 89.20 87.00 88.10
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Negations 71.70 70.90 71.30
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IgnoreVars 73.80 73.10 73.50
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Concepts 90.70 89.60 90.10
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Frames 88.50 87.90 88.20
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Reentrancies 70.40 71.80 71.10
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SRL 79.00 79.60 79.30
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=================== ========= ========= =========
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Note this parser does NOT perform wikification.
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'''
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AMR3_GRAPH_PRETRAIN_PARSER = HANLP_URL + 'amr/amr3_graph_pretrain_parser_20221207_153759.zip'
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'''A seq2seq (:cite:`bevilacqua-etal-2021-one`) BART (:cite:`lewis-etal-2020-bart`) large parser trained on Abstract
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Meaning Representation 3.0 (:cite:`knight2014abstract`) with graph pre-training (:cite:`bai-etal-2022-graph`).
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Its performance is ``84.3`` according to their official repository. Using ``amr-evaluation-enhanced``, the performance is
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slightly lower:
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=================== ========= ========= =========
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Metric P R F1
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=================== ========= ========= =========
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Smatch 84.4 83.6 84.0
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Unlabeled 86.7 85.8 86.2
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No WSD 84.9 84.1 84.5
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Non_sense_frames 91.8 91.6 91.7
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Wikification 83.6 81.7 82.6
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Named Ent. 89.3 87.4 88.4
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Negations 71.6 72.2 71.9
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IgnoreVars 74.6 74.2 74.4
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Concepts 90.7 90.0 90.3
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Frames 88.8 88.5 88.7
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Reentrancies 72.1 72.9 72.5
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SRL 80.1 80.7 80.4
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=================== ========= ========= =========
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Note this parser does NOT perform wikification.
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'''
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MRP2020_AMR_ENG_ZHO_XLM_BASE = 'http://download.hanlp.com/amr/extra/amr-eng-zho-xlm-roberta-base_20220412_223756.zip'
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'''A wrapper for the Permutation-invariant Semantic Parser (:cite:`samuel-straka-2020-ufal`) trained on MRP2020 English
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and Chinese AMR corpus. It was ranked the top in the MRP2020 competition, while this release is a base version.
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See the original paper for the detailed performance. Note this model requires tokens and lemmas (for English) to be
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provided as inputs.
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'''
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MRP2020_AMR_ZHO_MENGZI_BASE = 'http://download.hanlp.com/amr/extra/amr-zho-mengzi-base_20220415_101941.zip'
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'''A Chinese Permutation-invariant Semantic Parser (:cite:`samuel-straka-2020-ufal`) trained on MRP2020
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Chinese AMR corpus using Mengzi BERT base (:cite:`zhang2021mengzi`). Its performance on dev set is
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``{amr-zho [tops F1: 85.43%][anchors F1: 93.41%][labels F1: 87.68%][properties F1: 82.02%][edges F1: 73.17%]
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[attributes F1: 0.00%][all F1: 84.11%]}``. Test set performance is unknown since the test set is not released to the
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public.
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'''
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# Will be filled up during runtime
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ALL = {}
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