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import zipfile
import requests
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():
os.makedirs('./cache', exist_ok=True)
path = './cache/crawl-300d-2M-subword.bin'
if not os.path.exists(path):
url = 'https://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M-subword.zip'
filename = os.path.basename(url)
_path = f"./cache/{filename}"
with open(_path, "wb") as f:
r = requests.get(url)
f.write(r.content)
with zipfile.ZipFile(_path, 'r') as zip_ref:
zip_ref.extractall("./cache")
os.remove(_path)
return fasttext.load_facebook_model(path)
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)
def get_vector(_model, _word_a, _word_b):
# return np.mean([_model[_x] for _x in _word_a.split(" ")], axis=0) - np.mean([_model[_x] for _x in _word_b.split(" ")], axis=0)
return _model[_word_a] - _model[_word_b]
# load dataset
data = load_dataset("cardiffnlp/relentless", split="test")
full_result = []
os.makedirs("results/word_embedding/fasttext", exist_ok=True)
scorer = None
for d in data:
ppl_file = f"results/word_embedding/fasttext/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl"
anchor_embeddings = [(a, b) for a, b in d['prototypical_examples']]
option_embeddings = [(x, y) for x, y in d['pairs']]
if not os.path.exists(ppl_file):
if scorer is None:
scorer = load_model()
anchor_embeddings = [get_vector(scorer, a, b) for a, b in d['prototypical_examples']]
option_embeddings = [get_vector(scorer, x, y) for x, y in d['pairs']]
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": "fastText\textsubscript{pair}", "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("results/word_embedding/fasttext.csv")
df = (100 * df).round()
print(df.to_markdown())
print(df.to_latex())
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