bertin-roberta-base-spanish / get_embeddings_and_perplexity.py
edugp's picture
Add script to generate dataset of embeddings and perplexities. Add script to generate t-SNE plot for embedding and perplexity visualization.
a81e575
1.53 kB
#!/usr/bin/env python
import kenlm
from datasets import load_dataset
from tqdm import tqdm
import pandas as pd
import numpy as np
from sentence_transformers import SentenceTransformer
TOTAL_SENTENCES = 20000
def pp(log_score, length):
return 10.0 ** (-log_score / length)
embedder = "distiluse-base-multilingual-cased-v1"
embedder_model = SentenceTransformer(embedder)
embedding_shape = embedder_model.encode(["foo"])[0].shape[0]
# http://dl.fbaipublicfiles.com/cc_net/lm/es.arpa.bin
model = kenlm.Model("es.arpa.bin")
mc4 = load_dataset("mc4", "es", streaming=True)
count = 0
embeddings = []
lenghts = []
perplexities = []
sentences = []
for sample in tqdm(mc4["train"].shuffle(buffer_size=100_000), total=416057992):
lines = sample["text"].split("\n")
for line in lines:
count += 1
log_score = model.score(line)
length = len(line.split()) + 1
embedding = embedder_model.encode([line])[0]
embeddings.append(embedding.tolist())
perplexities.append(pp(log_score, length))
lenghts.append(length)
sentences.append(line)
if count == TOTAL_SENTENCES:
break
if count == TOTAL_SENTENCES:
embeddings = np.array(embeddings)
df = pd.DataFrame({"sentence": sentences, "length": lenghts, "perplexity": perplexities})
for dim in range(embedding_shape):
df[f"dim_{dim}"] = embeddings[:, dim]
df.to_csv("mc4-es-perplexity-sentences.tsv", index=None, sep="\t")
print("DONE!")
break