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import zipfile |
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import requests |
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import os |
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import json |
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from statistics import mean |
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import pandas as pd |
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from datasets import load_dataset |
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def load_model(): |
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from gensim.models import fasttext |
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os.makedirs('./cache', exist_ok=True) |
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path = './cache/crawl-300d-2M-subword.bin' |
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if not os.path.exists(path): |
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url = 'https://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M-subword.zip' |
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filename = os.path.basename(url) |
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_path = f"./cache/{filename}" |
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with open(_path, "wb") as f: |
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r = requests.get(url) |
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f.write(r.content) |
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with zipfile.ZipFile(_path, 'r') as zip_ref: |
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zip_ref.extractall("./cache") |
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os.remove(_path) |
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return fasttext.load_facebook_model(path) |
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def cosine_similarity(a, b): |
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norm_a = sum(map(lambda x: x * x, a)) ** 0.5 |
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norm_b = sum(map(lambda x: x * x, b)) ** 0.5 |
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return sum(map(lambda x: x[0] * x[1], zip(a, b)))/(norm_a * norm_b) |
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def get_vector(_model, _word_a, _word_b): |
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return _model[_word_a] - _model[_word_b] |
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data = load_dataset("cardiffnlp/relentless", split="test") |
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full_result = [] |
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os.makedirs("results/word_embedding/fasttext", exist_ok=True) |
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scorer = None |
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for d in data: |
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ppl_file = f"results/word_embedding/fasttext/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl" |
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anchor_embeddings = [(a, b) for a, b in d['prototypical_examples']] |
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option_embeddings = [(x, y) for x, y in d['pairs']] |
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if not os.path.exists(ppl_file): |
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if scorer is None: |
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scorer = load_model() |
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anchor_embeddings = [get_vector(scorer, a, b) for a, b in d['prototypical_examples']] |
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option_embeddings = [get_vector(scorer, x, y) for x, y in d['pairs']] |
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similarity = [[cosine_similarity(a, b) for b in anchor_embeddings] for a in option_embeddings] |
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output = [{"similarity": s} for s in similarity] |
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with open(ppl_file, "w") as f: |
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f.write("\n".join([json.dumps(i) for i in output])) |
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with open(ppl_file) as f: |
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similarity = [json.loads(i)['similarity'] for i in f.read().split("\n") if len(i) > 0] |
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true_rank = d['ranks'] |
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assert len(true_rank) == len(similarity), f"Mismatch in number of examples: {len(true_rank)} vs {len(similarity)}" |
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prediction = [max(s) for s in similarity] |
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rank_map = {p: n for n, p in enumerate(sorted(prediction, reverse=True), 1)} |
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prediction_max = [rank_map[p] for p in prediction] |
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prediction = [min(s) for s in similarity] |
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rank_map = {p: n for n, p in enumerate(sorted(prediction, reverse=True), 1)} |
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prediction_min = [rank_map[p] for p in prediction] |
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prediction = [mean(s) for s in similarity] |
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rank_map = {p: n for n, p in enumerate(sorted(prediction, reverse=True), 1)} |
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prediction_mean = [rank_map[p] for p in prediction] |
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tmp = pd.DataFrame([true_rank, prediction_max, prediction_min, prediction_mean]).T |
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cor_max = tmp.corr("spearman").values[0, 1] |
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cor_min = tmp.corr("spearman").values[0, 2] |
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cor_mean = tmp.corr("spearman").values[0, 3] |
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full_result.append({"model": "fastText\textsubscript{pair}", "relation_type": d['relation_type'], "correlation": cor_max}) |
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df = pd.DataFrame(full_result) |
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df = df.pivot(columns="relation_type", index="model", values="correlation") |
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df['average'] = df.mean(1) |
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df.to_csv("results/word_embedding/fasttext.csv") |
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df = (100 * df).round() |
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print(df.to_markdown()) |
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print(df.to_latex()) |
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