README.md

---
language:
- lt
- en
license: cc-by-2.5
size_categories:
- 1M<n<10M
dataset_info:
  features:
  - name: translation
    struct:
    - name: en
      dtype: string
    - name: lt
      dtype: string
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 850721410
    num_examples: 4948879
  - name: validation
    num_bytes: 8586743
    num_examples: 49989
  download_size: 643159722
  dataset_size: 859308153
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
---

![Scoris logo](https://scoris.lt/logo_smaller.png)

The data set is a merge of other open datasets:
- [wmt19](https://huggingface.co/datasets/wmt19) (lt-en)
- [opus100](https://huggingface.co/datasets/opus100) (en-lt)
- [sentence-transformers/parallel-sentences](https://huggingface.co/datasets/sentence-transformers/parallel-sentences)
  - Europarl-en-lt-train.tsv.gz
  - JW300-en-lt-train.tsv.gz
  - OpenSubtitles-en-lt-train.tsv.gz
  - Talks-en-lt-train.tsv.gz
  - Tatoeba-en-lt-train.tsv.gz
  - WikiMatrix-en-lt-train.tsv.gz
- Custom [Scoris](https://scoris.lt) data set translated using Deepl.

Basic clean-up and deduplication was applied when creating this set

This can be used to train Lithuanian-English-Lithuanian MT Seq2Seq models.

Made by [Scoris](https://scoris.lt) team

You can use this in the following way:
```python
from datasets import load_dataset
dataset_name = "scoris/en-lt-merged-data" 

# Load the dataset
dataset = load_dataset(dataset_name)

# Accessing data
# Display the first example from the training set
print("First training example:", dataset['train'][0])

# Display the first example from the validation set
print("First validation example:", dataset['validation'][0])

# Iterate through a few examples from the training set
for i, example in enumerate(dataset['train']):
    if i < 5:
        print(f"Training example {i}:", example)
    else:
        break

# If you want to use the dataset in a machine learning model, you can directly
# iterate over the dataset or convert it to a pandas DataFrame for analysis
import pandas as pd

# Convert the training set to a pandas DataFrame
train_df = pd.DataFrame(dataset['train'])
print(train_df.head())
```