init

README.md

@@ -39,13 +39,13 @@ THe test set is private at the moment.
 ### Dataset Summary
 
 
-| relation_type        |   val. |   test |
-|:---------------------|-------:|-------:|
-| friend/ally of       |     20 |     92 |
-| competitor/rival of  |     20 |     89 |
-| known for            |     20 |    108 |
-| influenced by        |     20 |     93 |
-| similar to           |     20 |     93 |
+| relation_type       |   val. |   test |
+|:--------------------|-------:|-------:|
+| competitor/rival of |     20 |     84 |
+| friend/ally of      |     19 |     88 |
+| influenced by       |     19 |     90 |
+| known for           |     18 |    105 |
+| similar to          |     19 |     89 |
 
 ## Dataset Structure
 ### Data Instances

experiments/baseline_fasttext.py

@@ -4,12 +4,13 @@ import os
 import json
 from statistics import mean
 import pandas as pd
-from gensim.models import fasttext
 from datasets import load_dataset
 
 
 # load fasttext
 def load_model():
+    from gensim.models import fasttext
+
     os.makedirs('./cache', exist_ok=True)
     path = './cache/crawl-300d-2M-subword.bin'
     if not os.path.exists(path):

experiments/baseline_fasttext_zeroshot.py

@@ -3,12 +3,13 @@ import requests
 import os
 import json
 import pandas as pd
-from gensim.models import fasttext
 from datasets import load_dataset
 
 
 # load fasttext
 def load_model():
+    from gensim.models import fasttext
+
     os.makedirs('./cache', exist_ok=True)
     path = './cache/crawl-300d-2M-subword.bin'
     if not os.path.exists(path):

experiments/baseline_lm_lc_zeroshot.py

@@ -14,7 +14,7 @@ prompt_dict = {
     "influenced by": "what has influenced different entities",
     "similar to": "examples of entities that are similar"
 }
-data = load_dataset("cardiffnlp/relentless_full", split="test")
+data = load_dataset("cardiffnlp/relentless", split="test")
 full_result = []
 for lm, ppl_class, batch, pretty_name in [
     ("google/flan-ul2", EncoderDecoderLM, 1, "Flan-UL2"),

experiments/baseline_lm_qa_zeroshot.py

@@ -14,7 +14,7 @@ prompt_dict = {
     "influenced by": "what has influenced different entities",
     "similar to": "examples of entities that are similar"
 }
-data = load_dataset("cardiffnlp/relentless_full", split="test")
+data = load_dataset("cardiffnlp/relentless", split="test")
 full_result = []
 for lm, ppl_class, batch, pretty_name in [
     ("google/flan-ul2", EncoderDecoderLM, 1, "Flan-UL2"),
@@ -28,6 +28,7 @@ for lm, ppl_class, batch, pretty_name in [
         ppl_file = f"results/lm_qa_zeroshot/{os.path.basename(lm)}/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl"
 
         if not os.path.exists(ppl_file):
+            input(ppl_file)
 
             if scorer is None:
                 if ppl_class is OpenAI:

experiments/baseline_relbert.py

@@ -3,7 +3,6 @@ import json
 import pandas as pd
 from statistics import mean
 from datasets import load_dataset
-from relbert import RelBERT
 
 
 def cosine_similarity(a, b):
@@ -27,6 +26,7 @@ for lm in ['base', 'large']:
         if not os.path.exists(ppl_file):
 
             if scorer is None:
+                from relbert import RelBERT
                 scorer = RelBERT(f"relbert/relbert-roberta-{lm}")
             anchor_embeddings = scorer.get_embedding(d['prototypical_examples'])
             option_embeddings = scorer.get_embedding(d['pairs'], batch_size=64)