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translator.py

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+from functools import partial
+
+import torch
+
+from datasets import load_dataset
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+
+model_name = "facebook/nllb-200-3.3B"  # "facebook/nllb-200-distilled-600M"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name, use_auth_token=True, torch_dtype=torch.float32)
+model.to(device, torch.float32, True)
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name, use_auth_token=True, src_lang="eng_Latn"
+)
+
+
+def to_lang_code(text, lang_code):
+    inputs = tokenizer(text, return_tensors="pt").to(device)
+    translated_tokens = model.generate(
+        **inputs,
+        forced_bos_token_id=tokenizer.lang_code_to_id[lang_code],
+        max_length=int(len(inputs.tokens()) * 1.5)  # 50% more tokens for the translation just in case
+    )
+    return tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
+
+
+if __name__ == "__main__":
+    languages = (("nb", "nob_Latn"), ("nn", "nno_Latn"))
+    ds = load_dataset("paws-x", "en")
+    dss = {}
+    for lang, translate_code in languages:
+        translate = partial(to_lang_code, lang_code=translate_code)
+        dss[lang] = ds.map(lambda example: {
+            "sentence1": translate(example["sentence1"]),
+            "sentence2": translate(example["sentence2"]),
+        }, desc=f"Translating to {lang}")
+        for split in ("test", "validation", "train"):
+            json_lines = dss[lang][split].to_pandas().to_json(orient='records', lines=True)
+            with open(f"{lang}_{split}.json", "w") as json_file:
+                json_file.write(json_lines)