Bokmaal version of xsum

nob/dataset_dict.json

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+{"splits": ["train", "validation", "test"]}

nob/nob_test.json.tar.gz

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nob/nob_train.json.tar.gz

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nob/nob_validation.json.tar.gz

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nob/test/dataset_info.json

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+{
+  "builder_name": "xsum",
+  "citation": "\n@article{Narayan2018DontGM,\n  title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},\n  author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},\n  journal={ArXiv},\n  year={2018},\n  volume={abs/1808.08745}\n}\n",
+  "config_name": "default",
+  "dataset_size": 532255381,
+  "description": "\nExtreme Summarization (XSum) Dataset.\n\nThere are three features:\n  - document: Input news article.\n  - summary: One sentence summary of the article.\n  - id: BBC ID of the article.\n\n",
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+  "homepage": "https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset",
+  "license": "",
+  "size_in_bytes": 789558247,
+  "splits": {
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+      "num_examples": 204045,
+      "dataset_name": "xsum"
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+      "dataset_name": "xsum"
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+      "dataset_name": "xsum"
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+}

nob/test/state.json

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nob/train/data-00000-of-00001.arrow

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nob/train/dataset_info.json

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+  "builder_name": "xsum",
+  "citation": "\n@article{Narayan2018DontGM,\n  title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},\n  author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},\n  journal={ArXiv},\n  year={2018},\n  volume={abs/1808.08745}\n}\n",
+  "config_name": "default",
+  "dataset_size": 532255381,
+  "description": "\nExtreme Summarization (XSum) Dataset.\n\nThere are three features:\n  - document: Input news article.\n  - summary: One sentence summary of the article.\n  - id: BBC ID of the article.\n\n",
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nob/train/state.json

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nob/validation/data-00000-of-00001.arrow

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nob/validation/dataset_info.json

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+  "builder_name": "xsum",
+  "citation": "\n@article{Narayan2018DontGM,\n  title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},\n  author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},\n  journal={ArXiv},\n  year={2018},\n  volume={abs/1808.08745}\n}\n",
+  "config_name": "default",
+  "dataset_size": 532255381,
+  "description": "\nExtreme Summarization (XSum) Dataset.\n\nThere are three features:\n  - document: Input news article.\n  - summary: One sentence summary of the article.\n  - id: BBC ID of the article.\n\n",
+  "download_checksums": {
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+  "homepage": "https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset",
+  "license": "",
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+      "dataset_name": "xsum"
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+      "dataset_name": "xsum"
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+}

nob/validation/state.json

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+{
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+    }
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+}

translator.py

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+import argparse
+import re
+from functools import partial
+from pathlib import Path
+from typing import Optional, Union
+
+import nltk
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def to_lang_code(texts, lang_code, model, tokenizer, max_words=500):
+    is_string = isinstance(texts, str)
+    if is_string:
+        texts = [texts]
+    batch_size = len(texts)
+    to_translate = []
+    merges = []
+    for index, text in enumerate(texts):
+        # Split in sentences if too long
+        merges.append(0)
+        if text.count(" ") > max_words:
+            sentences = nltk.sent_tokenize(text, "norwegian")
+            text_to_translate = ""
+            for sentence in sentences:
+                spaces = (text_to_translate + " " + sentence).count(" ")
+                if spaces >= max_words:
+                    to_translate.append(text_to_translate.strip())
+                    merges[-1] += 1
+                else:
+                    text_to_translate += sentence + " "
+        else:
+            to_translate.append(text)
+    translated_texts = []
+    # Split in batches for translation
+    to_translate_batchs = [to_translate[i:i + batch_size] for i in range(0, len(to_translate), batch_size)]
+    for to_translate_batch in to_translate_batchs:
+        inputs = tokenizer(to_translate_batch, return_tensors="pt", padding=True, truncation=True).to(DEVICE)
+        translated_tokens = model.generate(
+            **inputs,
+            forced_bos_token_id=tokenizer.lang_code_to_id[lang_code],
+            max_length=int(len(inputs.tokens()) * 1.25)  # 25% more tokens for the translation just in case
+        )
+        translated_texts += tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
+    # Merge outputs properly
+    outputs = []
+    for merge in merges:
+        output = ""
+        if merge:
+            for i in range(len(outputs), len(outputs) + merge):
+                output += translated_texts[i] + " "
+            outputs.append(output.strip())
+        else:
+            outputs.append(translated_texts[len(outputs)].strip())
+    return outputs[0] if is_string else outputs
+
+
+def main(
+    dataset_name: str,
+    dataset_columns: Union[list, tuple],
+    model_name: Optional[str]="facebook/nllb-200-3.3B",  # "facebook/nllb-200-distilled-600M"
+    model_revision: Optional[str]=None,
+    dataset_splits: Union[list, tuple]=("test", "validation", "train"),
+    dataset_config: Optional[str]=None,
+    dataset_revision: Optional[str]=None,
+    source_lang: Optional[str]="eng_Latn",
+    target_langs: Optional[Union[list, tuple]]=("nob_Latn", "nno_Latn"),
+    batch_size: Optional[int]=24,
+    output_dir: Optional[Path]=Path("./"),
+) -> None:
+
+    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, revision=model_revision, use_auth_token=True, src_lang=source_lang,
+    )
+
+    ds = load_dataset(dataset_name, name=dataset_config, revision=dataset_revision)
+    dss = {}
+    for lang_code in target_langs:
+        translate = partial(to_lang_code, lang_code=lang_code, model=model, tokenizer=tokenizer)
+        dss[lang_code] = ds.map(
+            lambda batch: {col: translate(batch[col]) for col in dataset_columns},
+            batched=True,
+            batch_size=batch_size,
+            desc=f"Translating to {lang_code}",
+        )
+        lang_code_short = re.split(r"[-_ /]", lang_code)[0]
+        dss[lang_code].save_to_disk(output_dir / lang_code_short, max_shard_size="1GB")
+        for split in dataset_splits:
+            json_filename = f"{lang_code_short}_{split}.json.tar.gz".lower()
+            dss[lang_code][split].to_pandas().to_json(
+                output_dir / lang_code_short / json_filename, orient='records', lines=True
+            )
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Translate datasets using Facebook's NLLB models")
+    parser.add_argument('dataset_name')
+    parser.add_argument('dataset_columns', help="Comma separated column names to translate")
+    parser.add_argument('--dataset_splits', default="test,validation,train", help="Comma separated splits to translate")
+    parser.add_argument('--dataset_config')
+    parser.add_argument('--dataset_revision')
+    parser.add_argument('--model_name', default="facebook/nllb-200-3.3B")
+    parser.add_argument('--model_revision')
+    parser.add_argument('--source_lang', default="eng_Latn")
+    parser.add_argument('--target_langs', default="nob_Latn,nno_Latn", help="Comma separated target languages to translate to")
+    parser.add_argument('--batch_size', '-bs', default=24, type=int, help='Number of inputs per batch for prediction')
+    parser.add_argument('--output_dir', '-o', default="./", type=str)
+    args = parser.parse_args()
+    main(
+        dataset_name=args.dataset_name,
+        dataset_columns=args.dataset_columns.split(","),
+        dataset_splits=args.dataset_splits.split(","),
+        dataset_config=args.dataset_config,
+        dataset_revision=args.dataset_revision,
+        model_name=args.model_name,
+        model_revision=args.model_revision,
+        source_lang=args.source_lang,
+        target_langs=args.target_langs.split(","),
+        batch_size=args.batch_size,
+        output_dir=Path(args.output_dir),
+    )