init

experiments/baseline_lm_lc.py

@@ -57,7 +57,7 @@ for lm, ppl_class, batch, pretty_name in [
                 else:
                     scorer = ppl_class(lm, device_map='auto', low_cpu_mem_usage=True, offload_folder=f"./offload_folder/{os.path.basename(lm)}")
 
-            content = "\n".join([f'* ["{a}", "{b}"]' for a, b in d['positive_examples']])
+            content = "\n".join([f'* ["{a}", "{b}"]' for a, b in d['prototypical_examples']])
             prompt_input = f"{prompt_dict[d['relation_type']]}:\n{content}"
             if ppl_class is LM:
                 prompt_input = [f'{prompt_input}\n* ["{x}", "{y}"]' for x, y in d['pairs']]

experiments/baseline_lm_qa.py

@@ -57,7 +57,7 @@ for lm, ppl_class, batch, pretty_name in [
                 else:
                     scorer = ppl_class(lm, device_map='auto', low_cpu_mem_usage=True, offload_folder=f"./offload_folder/{os.path.basename(lm)}")
 
-            proto = ",".join([f'["{a}", "{b}"]' for a, b in d['positive_examples']])
+            proto = ",".join([f'["{a}", "{b}"]' for a, b in d['prototypical_examples']])
             prefix = f"Answer the question by yes or no. We know that {proto} are examples of {prompt_dict[d['relation_type']]}."
             if ppl_class is LM or ppl_class is OpenAI:
                 prompt_input = [f'{prefix} Are ["{x}", "{y}"] {prompt_dict[d["relation_type"]]} as well?\n yes' for x, y in d['pairs']]

experiments/baseline_relbert.py

@@ -13,7 +13,7 @@ def cosine_similarity(a, b):
 
 
 # load dataset
-data = load_dataset("cardiffnlp/relentless_full", split="test")
+data = load_dataset("cardiffnlp/relentless", split="test")
 full_result = []
 
 for lm in ['base', 'large']:
@@ -57,8 +57,6 @@ for lm in ['base', 'large']:
         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})
-        # full_result.append({"model": f"relbert-roberta-{lm} (min)", "relation_type": d['relation_type'], "correlation": cor_min})
-        # full_result.append({"model": f"relbert-roberta-{lm} (mean)", "relation_type": d['relation_type'], "correlation": cor_mean})
 
 df = pd.DataFrame(full_result)
 df = df.pivot(columns="relation_type", index="model", values="correlation")