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--- |
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language: |
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- lt |
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- en |
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license: cc-by-2.5 |
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size_categories: |
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- 1M<n<10M |
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dataset_info: |
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features: |
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- name: translation |
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struct: |
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- name: en |
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dtype: string |
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- name: lt |
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dtype: string |
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- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 850721410 |
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num_examples: 4948879 |
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- name: validation |
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num_bytes: 8586743 |
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num_examples: 49989 |
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download_size: 643159722 |
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dataset_size: 859308153 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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--- |
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![Scoris logo](https://scoris.lt/logo_smaller.png) |
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The data set is a merge of other open datasets: |
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- [wmt19](https://huggingface.co/datasets/wmt19) (lt-en) |
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- [opus100](https://huggingface.co/datasets/opus100) (en-lt) |
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- [sentence-transformers/parallel-sentences](https://huggingface.co/datasets/sentence-transformers/parallel-sentences) |
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- Europarl-en-lt-train.tsv.gz |
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- JW300-en-lt-train.tsv.gz |
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- OpenSubtitles-en-lt-train.tsv.gz |
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- Talks-en-lt-train.tsv.gz |
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- Tatoeba-en-lt-train.tsv.gz |
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- WikiMatrix-en-lt-train.tsv.gz |
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- Custom [Scoris](https://scoris.lt) data set translated using Deepl. |
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Basic clean-up and deduplication was applied when creating this set |
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This can be used to train Lithuanian-English-Lithuanian MT Seq2Seq models. |
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Made by [Scoris](https://scoris.lt) team |
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You can use this in the following way: |
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```python |
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from datasets import load_dataset |
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dataset_name = "scoris/en-lt-merged-data" |
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# Load the dataset |
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dataset = load_dataset(dataset_name) |
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|
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# Accessing data |
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# Display the first example from the training set |
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print("First training example:", dataset['train'][0]) |
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# Display the first example from the validation set |
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print("First validation example:", dataset['validation'][0]) |
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|
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# Iterate through a few examples from the training set |
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for i, example in enumerate(dataset['train']): |
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if i < 5: |
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print(f"Training example {i}:", example) |
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else: |
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break |
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|
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# If you want to use the dataset in a machine learning model, you can directly |
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# iterate over the dataset or convert it to a pandas DataFrame for analysis |
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import pandas as pd |
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|
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# Convert the training set to a pandas DataFrame |
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train_df = pd.DataFrame(dataset['train']) |
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print(train_df.head()) |
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``` |