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())