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README.md

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----
-language:
-- tr
-thumbnail:
-tags:
-- dataset
-- turkish
-- ted-multi
-- cleaned
-
-license: apache-2.0
-datasets:
-- ted-multi
-
----
-
-# Turkish Ted talk translations 
-# Created from ted-multi dataset
-
-adding processing steps here if you want another language
-
-
-```python
-#using Turkish as target
-target_lang="tr"  # change to your target lang
-
-
-from datasets import load_dataset
-#ted-multi is a multiple language translated dataset
-#fits for our case , not to big and curated but need a simple processing
-
-dataset = load_dataset("ted_multi")
-dataset.cleanup_cache_files()
-
-#original from patrick's
-#chars_to_ignore_regex = '[,?.!\-\;\:\"“%‘”�—’…–]'  # change to the ignored characters of your fine-tuned model
-
-#will use cahya/wav2vec2-base-turkish-artificial-cv
-#checking inside model repository to find which chars removed (no run.sh)
-chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'
-
-
-import re
-
-def extract_target_lang_entries(batch):
-  #specific mapping for ted_multi dataset
-  #need to find index of language in each translation as it can shift
-  try:
-    target_index_for_lang= batch["translations"]["language"].index(target_lang) 
-  except ValueError:
-    #target not in list empty it for later processing
-    batch["text"] = None 
-    return batch 
-
-  #index_translation_pairs = zip(batch, target_index_for_batch)
-  text= batch["translations"]["translation"][target_index_for_lang] 
-  batch["text"] = re.sub(chars_to_ignore_regex, "", text.lower())
-  return batch
-
-
-#this dataset has additional columns need to say it
-cols_to_remove =  ['translations', 'talk_name']
-dataset = dataset.map(extract_target_lang_entries, remove_columns=cols_to_remove)
-
-
-#on preocessing we tagged None for empty ones
-dataset_cleaned = dataset.filter(lambda x: x['text'] is not None)
-dataset_cleaned
-
-from huggingface_hub import notebook_login
-
-notebook_login()
-
-dataset_cleaned.push_to_hub(f"{target_lang}_ted_talk_translated")
-
-```

dataset_infos.json

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-{"gorkemgoknar--tr_ted_talk_translated": {"description": "Massively multilingual (60 language) data set derived from TED Talk transcripts.\nEach record consists of parallel arrays of language and text. Missing and\nincomplete translations will be filtered out.\n", "citation": "@InProceedings{qi-EtAl:2018:N18-2,\n  author    = {Qi, Ye  and  Sachan, Devendra  and  Felix, Matthieu  and  Padmanabhan, Sarguna  and  Neubig, Graham},\n  title     = {When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?},\n  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},\n  month     = {June},\n  year      = {2018},\n  address   = {New Orleans, Louisiana},\n  publisher = {Association for Computational Linguistics},\n  pages     = {529--535},\n  abstract  = {The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases -- providing gains of up to 20 BLEU points in the most favorable setting.},\n  url       = {http://www.aclweb.org/anthology/N18-2084}\n}\n", "homepage": "https://github.com/neulab/word-embeddings-for-nmt", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "ted_multi_translate", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 14008706.562739734, "num_examples": 182470, "dataset_name": "tr_ted_talk_translated"}, "validation": {"name": "validation", "num_bytes": 288426.0869565217, "num_examples": 4045, "dataset_name": "tr_ted_talk_translated"}, "test": {"name": "test", "num_bytes": 369201.66796062666, "num_examples": 5029, "dataset_name": "tr_ted_talk_translated"}}, "download_checksums": null, "download_size": 12232993, "post_processing_size": null, "dataset_size": 14666334.317656882, "size_in_bytes": 26899327.317656882}}

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data/test-00000-of-00001.parquet → gorkemgoknar--tr_ted_talk_translated/parquet-validation.parquet

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