-
Notifications
You must be signed in to change notification settings - Fork 1
/
config.yaml
134 lines (115 loc) · 6.19 KB
/
config.yaml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
pipeline:
- "query_expansion"
- "chat_noir"
- "cleaning"
- "simple_term_ranking"
- "classifier_ranking"
- "remerging"
- "pickling"
chat_noir:
# enables the cache
is_caching_enabled: true
# filename of the cache results from the chat noir requests
cache_filename: "cache/chat_noir_cache.pkl"
# number of query retrying on receiving errors
number_of_query_retries: 4
# number of documents to retrieve per query
docs_per_query: 100
# all documents with a score lower than this, are going to be ignored, if = -1, this is not going to be used at all
score_threshold: -1
# all documents with a spam rank higher than this, are going to be ignored, if = -1, this is not going to be used at all
spam_threshold: -1
# all documents with a page rank lower than this are going to be ignored, if = -1, this is not going to be used at all
page_rank_treshold: -1
query_expansion:
enabled: true
# choose from:
# "en_mass" - retrieves the "nearest" synonym for ever noun/ajdective/verb and creates new queries with only one word changed to the synonym
# "only_nouns" - uses queries only with nouns and synonyms of these nouns
# "bilbo_baggins" use bilbo baggins approach (4 queries)
# "masked_model_expansion" uses a self trained masked BERT model to retrieve similar words, this mode is not included in the "combine_all" mode
# "text_generation" creates a text with a text-generation model, extracts the nouns and uses these nouns in the query
#
# this string can also be a comma-seperated list of the modes to combine multiple query expansion modes
#mode: "bilbo_baggins"
mode: "en_mass,bilbo_baggins,masked_model_expansion"
# whether or not include the default query
# this flag is usefull to test the expansion modes as 'raw' modes
use_default_query_everytime: true
# source of synonyms
# this option is only relevant for the following query expansion modes: en_mass, only_nouns, bilbo_baggins, combine_all
# Options:
# - pretrained_gensim (pretrained word-to-vec model)
# - selftrained_w2v (self-trained word-to-vec model)
#synonym_source: "pretrained_gensim"
synonym_source: "selftrained_w2v"
# minimal levenshtein distance between two words to be considered a synonym and not another form of the same word
min_levensthein_distance: 2
# for retrieving synonyms with w2v context words can be used to find better semantical-matching synonyms
# this value specifies how many additional context words should be used to find the synonyms
# this value can be at max the value of 'max_number_of_synonyms'
number_of_additional_context_words: 0
# specifies the max number of synonyms found for the synonym-related expansion modes
# trivially, more synonyms => more queries (at least for some modes like 'en_mass')
max_number_of_synonyms: 2
# the name of the model to be used for pretrained w2v
# this option is not relevant when using selftrained w2v model
# possible models are:
# - word2vec-google-news-300
# - fasttext-wiki-news-subwords-300
# - glove-twitter-300 (or glove-twitter-25 - which is smaller/ faster to load and nice for testing purposes)
# - conceptnet-numberbatch-17-06-300
# - glove-wiki-gigaword-300 -> is currently the best one
#gensim_model_name : "glove-twitter-25"
#gensim_model_name : "glove-wiki-gigaword-300"
gensim_model_name : "models/gensim-data/glove-wiki-gigaword-300"
# path to the masked language model or a pretrained model like 'roberta'
#masked_langue_model : "bert-base-uncased"
#masked_langue_model : "roberta-base"
masked_langue_model : "material/query-expansion-models/argsme-language-model/masked-language-model/model"
# the token that is used to replace words
# for bert = [MASK]
# for roberta = <mask>
masked_token : "[MASK]"
# path to the text-generation model or a pretrained model like 'gpt2'
text_generation_model : "gpt2"
#text_generation_model : "material/query-expansion-models/argsme-language-model/text-generation-model/model"
# If set to true, the first extracted feature is added after the query-question
use_text_generation_prefix : true
# path of the self-trained w2v model used for query expansion
# inside the '.../models/' folder are multiple models available
#path_w2v_trained_model : "material/query-expansion-models/domain-specific-w2v/models/argsme_large_200v-BOW-3c.model"
path_w2v_trained_model : "material/query-expansion-models/domain-specific-w2v/models/argsme_large_400v-BOW-3c.model"
remerging:
spam_rank: 0.1
page_rank: 0.1
score: 1
simple_terms: 0.2
classifier: 2
targer:
# Models for the TARGER API:
# Combo -> Classifies input text to argument structure (Combo model - big dataset) --- !!! works kinda !!!
# ES -> Classifies input text to argument structure (Essays model, fasttext embeddings) --- works kinda
# ES_dep -> Classifies input text to argument structure (Essays model, dependency based)
# IBM -> Classifies input text to argument structure (IBM model, fasttext - big dataset) works no errors
# NewPE -> Classifies input text to argument structure (Essays model, fasttext embeddings) --- !!! DOES NOT WORK - 504 timed out !!!
# NewWD -> Classifies input text to argument structure (Essays model, fasttext - big dataset) !!! DOES NOT WORK - 504 timed out !!!!
# WD -> Classifies input text to argument structure (WebD model, fasttext - big dataset) --- works, but very low recognition ratings
# WD_dep -> Classifies input text to argument structure (WebD model, dependency based)
model: "IBM"
# minimal confidence for the selected types as float
min_confidence: 0.8
# select for type of argumemt labels: claim, premise, both
arg_label: "both"
# default value for errors, if missing -1; normal value is between 0.0 (lowest in result) and 1.0 (highest possible result)
# 1 has no influence
error_default: 1.0
# True if (number of argument tagged words)/(number of words - punctuation), False (number of argument tagged words)/(number of words)
ignore_punctuation: true
dir:
topic_input: "./material"
qrels_input: "./material"
# run_name to give a specific pipeline "run" a distinct name
# works like a version specifier
# the name of the evaluation files and the cache files depend on this name
run_name: "test"