NLP, Deep Learning, Translation, Gpt 3, Data Science.
num_return_sequences=5) candidates = [gpt2_tokenizer.decode(output, skip_special_tokens=True) for output in gpt2_output] # Ranking candidate translations using BERT scores = [] for candidate in candidates: bert_input = bert_tokenizer.encode(f"[MASK] {source_idiom} -> {candidate}",