init

experiments/baseline_lm_lc.py

@@ -94,7 +94,7 @@ print(df)
 df = df.pivot(columns="relation_type", index="model", values="correlation")
 df = df.T[models].T
 df['average'] = df.mean(1)
-df.to_csv("experiments/results/lm_lc/lm.csv")
+df.to_csv("results/lm_lc/lm.csv")
 df = (100 * df).round()
 print(df.to_markdown())
 print(df.to_latex(escape=False))

experiments/baseline_lm_qa.py

@@ -91,7 +91,7 @@ models = df['model'].unique()
 df = df.pivot(columns="relation_type", index="model", values="correlation")
 df = df.T[models].T
 df['average'] = df.mean(1)
-df.to_csv("experiments/results/lm_qa/lm.csv")
+df.to_csv("results/lm_qa/lm.csv")
 df = (100 * df).round()
 print(df.to_markdown())
 print(df.to_latex(escape=False))

experiments/baseline_oracle.py

@@ -27,4 +27,4 @@ df = df.T
 df['average'] = df.mean(axis=1).round(1)
 print(df.to_markdown())
 print(df.to_latex())
-df.to_csv("experiments/results/oracle.csv")
+df.to_csv("results/oracle.csv")

experiments/baseline_relbert.py

@@ -17,7 +17,7 @@ data = load_dataset("cardiffnlp/relentless", split="test")
 full_result = []
 
 for lm in ['base', 'large']:
-    os.makedirs(f"./experiments/results/relbert/relbert-roberta-{lm}", exist_ok=True)
+    os.makedirs(f"results/relbert/relbert-roberta-{lm}", exist_ok=True)
     scorer = None
     for d in data:
         ppl_file = f"experiments/results/relbert/relbert-roberta-{lm}/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl"
@@ -61,7 +61,7 @@ for lm in ['base', 'large']:
 df = pd.DataFrame(full_result)
 df = df.pivot(columns="relation_type", index="model", values="correlation")
 df['average'] = df.mean(1)
-df.to_csv("experiments/results/relbert/relbert.csv")
+df.to_csv("results/relbert/relbert.csv")
 df = (100 * df).round()
 print(df.to_markdown())
 print(df.to_latex())

experiments/baseline_relbert_misc.py

@@ -1,67 +0,0 @@
-import os
-import json
-import pandas as pd
-from statistics import mean
-from datasets import load_dataset
-from relbert import RelBERT
-
-
-def cosine_similarity(a, b):
-    norm_a = sum(map(lambda x: x * x, a)) ** 0.5
-    norm_b = sum(map(lambda x: x * x, b)) ** 0.5
-    return sum(map(lambda x: x[0] * x[1], zip(a, b))) / (norm_a * norm_b)
-
-
-# load dataset
-data = load_dataset("cardiffnlp/relentless_full", split="test")
-full_result = []
-
-for lm in ['relbert-roberta-base-nce-t-rex', 'relbert-roberta-base-nce-nell']:
-    os.makedirs(f"./experiments/results/relbert/{lm}", exist_ok=True)
-    scorer = None
-    for d in data:
-        ppl_file = f"experiments/results/relbert/{lm}/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl"
-        anchor_embeddings = [(a, b) for a, b in d['positive_examples']]
-        option_embeddings = [(x, y) for x, y in d['pairs']]
-
-        if not os.path.exists(ppl_file):
-
-            if scorer is None:
-                scorer = RelBERT(f"relbert/{lm}")
-            anchor_embeddings = scorer.get_embedding(d['positive_examples'])
-            option_embeddings = scorer.get_embedding(d['pairs'], batch_size=64)
-            similarity = [[cosine_similarity(a, b) for b in anchor_embeddings] for a in option_embeddings]
-            output = [{"similarity": s} for s in similarity]
-            with open(ppl_file, "w") as f:
-                f.write("\n".join([json.dumps(i) for i in output]))
-
-        with open(ppl_file) as f:
-            similarity = [json.loads(i)['similarity'] for i in f.read().split("\n") if len(i) > 0]
-
-        true_rank = d['ranks']
-        assert len(true_rank) == len(similarity), f"Mismatch in number of examples: {len(true_rank)} vs {len(similarity)}"
-        prediction = [max(s) for s in similarity]
-        rank_map = {p: n for n, p in enumerate(sorted(prediction, reverse=True), 1)}
-        prediction_max = [rank_map[p] for p in prediction]
-
-        prediction = [min(s) for s in similarity]
-        rank_map = {p: n for n, p in enumerate(sorted(prediction, reverse=True), 1)}
-        prediction_min = [rank_map[p] for p in prediction]
-
-        prediction = [mean(s) for s in similarity]
-        rank_map = {p: n for n, p in enumerate(sorted(prediction, reverse=True), 1)}
-        prediction_mean = [rank_map[p] for p in prediction]
-
-        tmp = pd.DataFrame([true_rank, prediction_max, prediction_min, prediction_mean]).T
-        cor_max = tmp.corr("spearman").values[0, 1]
-        cor_min = tmp.corr("spearman").values[0, 2]
-        cor_mean = tmp.corr("spearman").values[0, 3]
-        full_result.append({"model": f"RelBERT\textsubscript{'{'}{lm.upper()}{'}'}", "relation_type": d['relation_type'], "correlation": cor_max})
-
-df = pd.DataFrame(full_result)
-df = df.pivot(columns="relation_type", index="model", values="correlation")
-df['average'] = df.mean(1)
-df.to_csv("experiments/results/relbert/relbert_misc.csv")
-df = (100 * df).round()
-print(df.to_markdown())
-print(df.to_latex())

experiments/results/oracle.csv

@@ -0,0 +1,2 @@
+,competitor/rival of,friend/ally of,influenced by,known for,similar to,average
+Avg.\ of others,0.7743314787749431,0.7854235774817461,0.7150896845459409,0.8236406723889734,0.8064321816808586,0.8