id string | kab_tfng string | kab_latn string | en string |
|---|---|---|---|
1567385 | ⵜⵛⴼⵉⴹ ⴼⵍⵍⵉ ? | Tecfiḍ fell-i ? | Do you remember me? |
7043947 | ⴰⵔ ⵜⵉⵎⵍⵉⵍⵉⵜ! | Ar timlilit! | Good-bye! |
2294332 | ⵜⵙⵙⴰⵡⵍⴹ ⴰⵙ ⵉ ⵓⵎⴷⴷⴰⴽⵍ ⵉⴽ ⵉⵍⵍⴰⵏ ⴷⴳ ⴽⴰⵏⴰⴷⴰ? | Tessawleḍ-as i wemddakel-ik yellan deg Kanada? | Did you call your friend in Canada? |
2294528 | ⴰⵍⵍⵏ ⵉⵡ ⴷ ⵜⵉⴱⵔⴽⴰⵏⵉⵏ. | Allen-iw d tiberkanin. | I have black eyes. |
2294620 | ⵓⵖⴻⵖ ⴷ ⵜⴰⵢⵓⴳⴰ ⵏ ⵡⴰⵔⴽⴰⵙⵏ. | Uɣeɣ-d tayuga n warkasen. | I bought a pair of shoes. |
2296647 | ⴰⵙⴳⵣⴰⵡⴰⵍ ⴰ, ⵏ ⵓⵍⵜⵎⴰ. | Asegzawal-a, n weltma. | This dictionary is my sister's. |
2771352 | ⵓⵔ ⵜⵜⵛⵉⵖ ⴰⵔⴰ. | Ur ččiɣ ara. | I haven't eaten. |
2771573 | ⴰⵎⵎⵓⵙ ⵉⴳⵍⵍⵓ ⴷ ⵙ ⵡⴰⵟⵟⴰⵏⴻⵏ. | Ammus igellu-d s waṭṭanen. | Filth breeds illnesses. |
2789494 | ⵓⵔⵄⴰⴷ ⵖⵔⵉⵖ ⴰⵖⵎⵉⵙ ⵏ ⵡⴰⵙⵙ ⴰ. | Urεad ɣriɣ aɣmis n wass-a. | I haven't read today's newspaper yet. |
2790536 | ⵖⵓⵔⵙ ⵙⵉⵏ ⵉⵎⵛⴰⵛ : ⵢⵉⵡⵏ ⴷ ⴰⴱⵔⴽⴰⵏ ⵡⴰⵢⴹ ⴷ ⴰⵎⵍⵍⴰⵍ. | Ɣur-s sin yemcac : yiwen d aberkan wayeḍ d amellal. | He keeps two cats: one is black, and the other white. |
2807521 | ⵉⵙⵙⵔⵖ ⴰⵅⵅⴰⵎ ⵉⵙ. | Yesserɣ axxam-is. | He set his house on fire. |
3675223 | ⵖⵓⵔⵡⵏⵜ ⵙⴱⵄⴰ ⵡⴰⵔⵔⴰⵛ. | Ɣur-went sebεa warrac. | You have seven children. |
3678725 | ⵜⵓⵔⴰ, ⵓⵃⵡⴰⴷⵊⵖ ⵟⵓⵎ. | Tura, uḥwaǧeγ Ṭum. | I need Tom now. |
3678730 | ⵟⵓⵎ, ⵓⵃⵡⴰⴷⵊⵖ ⵜ ⵜⵓⵔⴰ. | Ṭum, uḥwaǧeγ-t tura. | I need Tom now. |
3678732 | ⵓⵃⵡⴰⴷⵊⵖ ⵟⵓⵎ ⵜⵓⵔⴰ. | Uḥwaǧeγ Ṭum tura. | I need Tom now. |
3678735 | ⵓⵃⵡⴰⴷⵊⵖ ⵜⵓⵔⴰ ⵟⵓⵎ | Uḥwaǧeγ tura Ṭum | I need Tom now. |
3679112 | ⵉⵙⵎ ⵉⵡ ⵢⵓⴱⴰ. | Isem-iw Yuba. | My name is Juba. |
2295741 | ⵉⴷⴷⵔⵖⵍ. | Yedderɣel. | He went blind. |
4714129 | ⵢⵢⴰⵖ ⴰⴷ ⵜⵛⵛⴹ! | Yyaɣ ad tecceḍ! | Come eat! |
2771621 | ⴳⵔⵔⵣⵖ. | Gerrzeɣ. | I'm fine. |
5507673 | ⵙⵍⵉⵖ ⴷ ⴰⴽⴽⵏ ⵏⵏⵉⴹⵏ. | Sliɣ-d akken nniḍen. | I heard otherwise. |
5507689 | ⵓⵔ ⵣⵎⵉⵔⵖ ⴰⵔⴰ ⴰⴷ ⵅⵎⵎⵖ ⴰⴽⴽⵏ ⵏⵏⵉⴹⵏ. | Ur zmireɣ ara ad xemmeɣ akken nniḍen. | I can't think otherwise. |
5507691 | ⵓⵔ ⵣⵎⵉⵔⵖ ⴰⵔⴰ ⴰⴷ ⵅⴷⵎⵖ ⴰⴽⴽⵏ ⵏⵏⵉⴹⵏ. | Ur zmireɣ ara ad xedmeɣ akken nniḍen. | I couldn't do otherwise. |
5507692 | ⵎⵓⵃⴰⵍ ⴰⴷ ⵜⵅⵎⵎⴹ ⴰⴽⴽⵏ ⵏⵏⵉⴹⵏ. | Muḥal ad txemmeḍ akken nniḍen. | Don't ever think otherwise. |
5507698 | ⵓⵔ ⵉⵢⵉ ⵉⵖⵍⵍⴱ ⵢⵉⵡⵏ ⴰⴽⴽⵏ ⵏⵏⵉⴹⵏ. | Ur iyi-iɣelleb yiwen akken nniḍen. | Nobody will convince me otherwise. |
5507702 | ⵢⵉⵡⵏ ⵓⵔ ⵉⵣⵎⵉⵔ ⴰⴷ ⵉⵢⵉ ⵉⵖⵍⴱ ⴰⴽⴽⵏ ⵏⵏⵉⴹⵏ. | Yiwen ur yezmir ad iyi-iɣleb akken nniḍen. | Nobody can convince me otherwise. |
5507718 | ⵄⵕⴹⵖ ⴰⴷ ⵖⵍⴱⵖ ⵟⵓⵎ ⴰⴽⴽⵏ ⵏⵏⵉⴹⵏ. | Ɛerḍeɣ ad ɣelbeɣ Tom akken nniḍen. | I tried to convince Tom otherwise. |
5507911 | ⴰⵣⵣⵍ ⵖⵉⵡⵍ, ⵎⵓⵍⴰⵛ ⴰⴷ ⴰⴽ ⵉⴷⴷⵊ ⵍⴽⴰⵕ. | Azzel ɣiwel, mulac ad ak-yeǧǧ lkaṛ. | Run fast, otherwise you will miss the bus. |
5507920 | ⴰⵣⵣⵍ ⵄⵊⵍ, ⵎⵓⵍⴰⵛ ⴰⴷ ⴰⴽ ⵜⴷⴷⵊ ⵜⵎⴰⵛⵉⵏⵜ. | Azzel ɛjel, mulac ad ak-teǧǧ tmacint. | Run faster, otherwise you will miss the train. |
5507925 | ⵄⵊⵍ, ⵎⵓⵍⴰⵛ ⵓⵔ ⵜⴻⵜⵜⵍⴰⵃⴰⵇⴹ ⴰⵔⴰ ⵍⴼⴰⴹⵓⵕ. | Ɛjel, mulac ur tettlaḥaqeḍ ara lfaḍur. | Hurry up, otherwise you'll be late for lunch. |
5507938 | ⵜⵓⵔⴰ ⵎⵉ ⵎⵇⵇⵔⵖ, ⵜⵜⵅⵎⵎⵉⵎⵖ ⵖⴼ ⵡⴰⵢⵏ ⵏⵏⵉⴹⵏ. | Tura mi meqqreɣ, ttxemmimeɣ ɣef wayen nniḍen. | Now that I'm a grownup, I think otherwise. |
5507946 | ⵏⵜⵜⴰ ⴷ ⴰⵜⵛⴰⵇⵍⴰⵍ, ⵎⵄⵏⴰ ⴷ ⴰⵇⵛⵉⵛ ⵉⵍⵀⴰⵏ ⴷⴳ ⵡⴰⵢⵏ ⵏⵏⵉⴹⵏ. | Netta d ačaqlal, meɛna d aqcic yelhan deg wayen nniḍen. | He is noisy, but otherwise a very nice boy. |
5508028 | ⴰⵣⴰⵍ ⵏ ⵓⵎⵍⵢⴰⵕ ⵉ ⵉⵀⴷⴷⵕⵏ ⵜⴰⴳⵍⵉⵣⵉⵜ. | Azal n umelyaṛ i iheddṛen taglizit. | English is spoken by about a billion people. |
2799850 | ⵉⵡⵡⴰ? | Yewwa? | Is it baked? |
2296134 | ⴼⴽ ⵉⵢⵉ ⴷ ⴰⵎⴷⵢⴰ. | Efk-iyi-d amedya. | Give me an example. |
9472533 | ⵎⵥⵥⵉⵢⵖ. | Meẓẓiyeɣ. | I am young. |
6707613 | ⴰⴽⴷⴷⴰⴼ! | Akeddav! | Liar! |
6715863 | ⵙⴰⵎⵉ ⵓⵔ ⵉⵏⵖⵉ ⴰⵔⴰ ⵉⵎⴰⵏ ⵉⵙ. | Sami ur yenɣi ara iman-is. | Sami didn't kill himself. |
6715871 | ⴳⵣⵎⵖ ⵜ ⵖⴼ ⵙⵉⵏ ⵉⵎⵎⵓⵔⵏ. | Gezmeɣ-t ɣef sin immuren. | I've cut it into two pieces. |
6715871 | ⴳⵣⵎⵖ ⵜ ⵖⴼ ⵙⵉⵏ ⵉⵎⵎⵓⵔⵏ. | Gezmeɣ-t ɣef sin immuren. | I cut it into two pieces. |
6715880 | ⵙⴰⵎⵉ ⵉⵎⵎⵓⴳⵔ ⴷ ⵍⴰⵢⵍⴰ. | Sami yemmuger-d layla. | Sami met Layla. |
6715886 | ⵙⴰⵎⵉ ⵢⵙⵃⴱⵉⴱⵉⵔ ⵖⴼ ⵍⴰⵢⵍⴰ. | Sami yseḥbibir ɣef Layla. | Sami cared about Layla. |
6715888 | ⵙⴰⵎⵉ ⵢⵓⴽⵔ ⵜⵉⵖⴰⵡⵙⵉⵡⵉⵏ ⵙⴳ ⵓⵅⵅⴰⵎ ⵏ ⵍⴰⵢⵍⴰ. | Sami yuker tiɣawsiwin seg uxxam n Layla. | Sami stole items from Layla's home. |
6715889 | ⵙⴰⵎⵉ ⵉⵏⵏⴰ ⴷ ⵖⴼ ⵍⴷⵊⴰⵍ ⵉⵙ ⵉ ⵉⵎⵎⵓⵜ ⴱⴰⴱⴰⵙ | Sami yenna-d ɣef lǧal-is i yemmut baba-s | Sami said it was his fault that his father was dead. |
6715894 | ⵙⴰⵎⵉ ⵉⵙⵡⴰ ⵉⵔⵏⴰ ⵉⴽⵉⵢⴼ. | Sami yeswa yerna ikeyyef. | Sami drank and smoked. |
6715898 | ⵙⴰⵎⵉ ⵓⵔ ⵉⵡⴰⵍⴰ ⴰⵔⴰ ⵢⴰⵔⴽ ⵍⴰⵢⵍⴰ ⵜⴻⵜⵜⵔⵓ. | Sami ur iwala ara yark Layla tettru. | Sami didn't see Layla cry at all. |
6715904 | ⵙⴰⵎⵉ ⵓⵔ ⴷ ⵉⵔⵓ ⵢ ⴰⵔⴰ ⵢⴰⵔⴽ ⴷⵉ ⵍⴷⵊⴰⵏⴰⵣⴰ. | Sami ur-d iru y-ara yark di lǧanaza. | Sami didn't cry at the funeral. |
6715924 | ⵙⴰⵎⵉ ⵢⵓⴹⵏ. | Sami yuḍen. | Sami got sick. |
6715928 | ⵙⴰⵎⵉ ⵉⵜⵇⵍⵍⴰⵇ ⴰⵟⴰⵙ ⵉⵔⵏⴰ ⵢⵓⴳⴰⴷ. | Sami yetqellaq aṭas yerna yugad. | Sami was very nervous and scared. |
6715930 | ⵙⴰⵎⵉ ⵉⵜⵇⵍⵍⴰⵇ ⴰⵟⴰⵙ. | Sami yetqellaq aṭas. | Sami was extremely nervous. |
6715931 | ⵙⴰⵎⵉ ⵉⵙⵙⴰⵡⵍ ⵉ ⴱⵓⴳⴰⵟⵓ. | Sami issawel i bugaṭu. | Sami hired a lawyer. |
6715932 | ⵙⴰⵎⵉ ⵉⵙⵙⴰⵡⵍ ⵉ ⵉⵎⵎⴰⵙ. | Sami issawel i yemma-s. | Sami called his mother. |
6715934 | ⵙⴰⵎⵉ ⵉⵙⵙⴽⵔⴰ ⴰⵅⵅⴰⵎ ⵉ ⵍⴰⵢⵍⴰ. | Sami yessekra axxam i Layla. | Sami rented the house for Layla. |
6715936 | ⵉⵣⵎⵔ ⴷ ⵙⴰⵎⵉ ⵉ ⵉⵏⵖⴰⵏ ⵍⴰⵢⵍⴰ | Izmer d Sami i yenɣan Layla | Sami could have murdered Layla. |
6715937 | ⵙⴰⵎⵉ ⵉⵇⵔⵃⵉⵜ ⵓⵄⵔⵓⵔ ⵉⵙ. | Sami iqerḥit weɛrur-is. | Sami had a bad back. |
6715939 | ⵙⴰⵎⵉ ⴷ ⵜⵉⵎⵏⵖⵉⵡⵜ ⵉ ⵉⵏⵖⴰ ⵉⵎⴰⵏ ⵉⵙ. | Sami d timenɣiwt i yenɣa iman-is. | Sami's death was a suicide. |
6715941 | ⵙⴰⵎⵉ ⵉⵥⵥⴰ ⵜⵉⵊⴷⴷⵊⵉⴳⵉⵏ. | Sami yeẓẓa tijeǧǧigin. | Sami planted flowers. |
6715943 | ⵙⴰⵎⵉ ⵉⵙⵄⴰ ⴰⴽⴽⵏ ⵙⵜⵜⴰ ⵉⵙⴳⴳⴰⵙⵏ. | Sami yesɛa akken setta iseggasen. | Sami was about six. |
6715944 | ⴰⵅⵏⴼⵓⵛ ⵏ ⵙⴰⵎⵉ ⵉⵍⵍⴰ ⵉⵍⴷⵉ. | Axenfuc n Sami yella yeldi. | Sami's mouth was open. |
6715945 | ⵙⴰⵎⵉ ⵢⵓⵎⵏ ⵉⵎⵎⴰⵙ. | Sami yumen yemma-s. | Sami trusted his mom. |
6715947 | ⵙⴰⵎⵉ ⵉⵔⵓⵃ ⵙ ⵓⵙⵓ. | Sami iruḥ s usu. | Sami went to bed. |
6715950 | ⵙⴰⵎⵉ ⵉⵍⵍⴰ ⴷⵉ ⵜⴰⵀⵉⵜⵉ. | Sami yella di Tahiti. | Sami was in Tahiti. |
6715951 | ⵙⴰⵎⵉ ⵢⵓⴽⵔ ⵜⴰⵎⴷⴰⴽⵍⵜ ⵏ ⴼⴰⵔⵉⴷ. | Sami yuker tamdakelt n Farid. | Sami stole Farid's girlfriend. |
6715954 | ⵙⴰⵎⵉ ⵓⵔ ⵢⵓⵎⵉⵏ ⴰⵔⴰ ⵍⴰⵢⵍⴰ ⵎⴰⵣⴰⵍ ⵜⴷⴷⵔ. | Sami ur yumin ara Layla mazal tedder. | Sami couldn't believe Layla was alive. |
6715956 | ⵙⴰⵎⵉ ⴷ ⴼⴰⵔⵉⴷ ⵍⵍⴰⵏ ⴰⴽⴽⵏ ⴷⵉ ⵍⵃⴱⵙ. | Sami d Farid llan akken di lḥebs. | Sami and Farid shared the same prison cell. |
6715962 | ⵙⴰⵎⵉ ⵉⵙⵡⴰ ⵔⴷ ⴱⵓⵍⵍ. | Sami yeswa Red Bull. | Sami drank Red Bull. |
6715963 | ⵙⴰⵎⵉ ⵉⵍⵍⴰ ⵉⵜⵜⵉⴷⵉⵔ ⴰⴽⴽⴷ ⵅⴰⵍⵜⵉⵙ. | Sami yella yettidir akked xalti-s. | Sami lived with his aunt. |
7000984 | ⴰⵍⵍⴰ ⵢⵉⵡⵏ ⵓⵎⵙⵉⵎⵖⵔ ⵉⵜⴷⴷⵓⵏ. | Alla yiwen umsimɣer iteddun. | Only one speaker is working. |
7014554 | ⴱⴷⴻⴷ ! | Bded ! | Get up! |
7014556 | ⵟⵟⴼ ⵉⵜ ⵉⴷ. | Ṭṭef-it-id. | Grab him. |
7014560 | ⵜⴻⵜⵜⴷⴷⵓ. | Tetteddu. | She walks. |
7014562 | ⵉⴳⵔⵔⵣ | Igerrez | Well done! |
7014565 | ⵓⵍⴰⵛ ⴰⵖⵉⵍⵉⴼ | Ulac aɣilif | No problem. |
7014560 | ⵜⴻⵜⵜⴷⴷⵓ. | Tetteddu. | It works. |
7014571 | ⵜⴻⵜⵜⵔⵓ ⵉⵎⵎⴰ. | Tettru yemma. | Mama cried. |
7014572 | ⴷⴷⵎ ⵉⵜ. | Ddem-it. | Take it. |
7014574 | ⵉⵏⵉ ⵢⵉ ⴷ. | Ini-yi-d. | Tell me. |
2294723 | ⵓⵏⴼⵏ ⵉⵢⵉ ⴰⴷ ⵔⵓⵃⵖ. | Unfen-iyi ad ruḥeɣ. | They let me go. |
7100797 | ⴷⴷⵊⴰⵏ ⵉⵢⵉ ⴰⴷ ⵔⵓⵃⵖ. | Ǧǧan-iyi ad ruḥeɣ. | They let me go. |
7024394 | ⵜⵉⴽⵜⵉ ⵉⵏⵓ ⵜⵎⵅⴰⵍⴰⴼ ⵖⴼ ⵜⵉⵏ ⵉⵏⴽ. | Tikti-inu temxalaf ɣef tin-inek. | I think different from you. |
7024422 | ⵓⵔⴷⵊⵉⵏ ⴰⴷ ⵏⴰⵃⴱⵙ ⴰⵍⵎⴰⴷ. | Werǧin ad naḥbes almad. | We never stop learning. |
7026877 | ⴷⴷⵊ ⴰⵇⵊⵓⵏ ⴷⴳ ⴱⵕⵕⴰ. | Eǧǧ aqjun deg beṛṛa. | Leave the dog outside. |
7026879 | ⵜⵣⵎⵕⴹ ⴷⴰⵖⵏ ⴰⴷ ⵜⵎⵙⵍⴰⵢⴹ ⵜⴰⴼⵔⴰⵏⵚⵉⵙⵜ? | Tzemreḍ daɣen ad tmeslayeḍ Tafranṣist? | Can you also speak French? |
7028101 | ⵉⵍⵍⴰ ⵡⴰⵙⵙ ⵉⴷⴳ ⵉⵎⵛⵜⵜⵛⴰⵡ ⵢⵉⵡⵏ ⴷ ⵓⵡⴰⵖⵣⵏ? | Yella wass ideg yemceččaw yiwen d uwaɣzen? | Has anyone ever faced an ogre? |
7027275 | ⵉⵍⵍⴰ ⵡⴰⵙ ⵉⴷⴳ ⵜⵏⵏⵓⵖⴹ ⴷ ⵓⵡⴰⵖⵣⵏ? | Yella was ideg tennuɣeḍ d uwaɣzen? | Have you ever fought with an ogre? |
7028506 | ⵡⵉⵏ ⵉⵇⵇⵙ ⵓⵣⵔⵎ, ⵉⵜⵜⴰⴳⵡⴰⴷ ⴰⵙⵖⵡⵏ. | Win yeqqes uzrem, yeţţagwad aseɣwen. | Whoever has been bitten by a snake fears the slightest stick. |
7028589 | ⵃⵓⵙⵖ ⵛⵡⵉⵢⴰ ⴽⴰⵏ ⵉ ⵎⵉ ⵓⵔ ⵣⵎⵉⵔⵖ ⴰⵔⴰ ⴰⴷ ⴷ ⴰⵙⵖ ⴰⵙⵙ ⴰ. | Ḥuseɣ cwiya kan i mi ur zmireɣ ara ad d-aseɣ ass-a. | I feel bad that I can't come today. |
7028592 | ⵜⴻⵜⵜⴰⵕⵓⴹ ⵜⴰⴱⵔⴰⵜ? | Tettaruḍ tabrat? | Are you writing a letter? |
7028594 | ⴷ ⵜⴰⴱⵔⴰⵜ ⵉ ⵜⴻⵜⵜⴰⵕⵓⴹ? | D tabrat i tettaruḍ? | Are you writing a letter? |
7028599 | ⵉⵜⵜⴱⵉⵏ ⴷ ⴷ ⵉⵍⵎⵥⵉ, ⵓⵔ ⵉⵣⵎⵉⵔ ⴰⵔⴰ ⴰⴷ ⵢⵉⵍⵉ ⴰⴽⵜⵔ ⵉⵡ. | Ittbin-d d ilemẓi, ur izmir ara ad yili akter-iw. | He looks young. He cannot be older than I. |
7028617 | ⴰⵏⵡⴰ ⴰⵔ ⴰⴷ ⵜⵃⵎⵎⵍⴹ, ⵎⴰ ⵜⵓⵖ ⵜⵎⴰⵍⴹ? | Anwa ar ad tḥemmeleḍ, ma tuɣ tmaleḍ? | We no longer love when we are in love. |
7028640 | ⴰⵎⴽ ⴰⵔⴰ ⵜⵃⵎⵎⵍⴹ ⵜⴰⵇⵛⵉⵛⵜ ⵉⴷⴳ ⵓⵔ ⴷ ⵉⴱⴰⵏ ⵡⴰⵔⴰ? | Amek ara tḥemmleḍ taqcict ideg ur-d iban wara? | How can one love a girl who remains hidden? |
7028649 | ⴰⵎⴰⵏ ⵓⵔ ⴷ ⵜⵓⴳⵉⵎ ⵉⵎⵎⴰ, ⵓⵔ ⵜⴽⴽⵙⵏ ⴼⴰⴷ ⴰⵢ ⴰⵜⵎⴰ. | Aman ur-d tugim yemma, ur tekkesen fad ay-atma. | The water that my mother will not give me from her jug will never quench my thirst. |
7030045 | ⵍⴰⵢⵍⴰ ⵜⵙⵍⴰ ⵉ ⵍⵀⴰⵔⴰⴷⵊ. | Layla tesla i lharaǧ. | Layla heard the noise. |
7030050 | ⵍⵀⴰⵔⴰⴷⵊ ⴰⴳⵉ ⵉⵙⵄⵓⵥⵓⴳ ⵉⵎⵥⴰⵖ | Lharaǧ-agi yesɛuẓug imeẓaɣ | This noise is deafening. |
7030054 | ⵍⴰⵢⵍⴰ ⵜⵙⵍⴰ ⵉ ⵍⵀⴰⵔⴰⴷⵊ ⴷⵉ ⴱⵕⵕⴰ. | Layla tesla i lharaǧ di beṛṛa. | Layla heard a noise outside. |
7030122 | ⵙⵓ ⴰⵎⴰⵏ. | Sew aman. | Drink water. |
7030125 | ⵉⵎⵎⵓⵜ? | Yemmut? | Dead? |
7030126 | ⵜⵎⵎⵓⵜ? | Temmut? | Dead? |
7030130 | ⵜⵎⵎⵓⵜⴹ? | Temmuteḍ? | Are you dead? |
Dataset Card for Kabyle Latin-to-Tifinagh Parallel Corpus
This dataset provides a parallel corpus of the Kabyle language (Taqbaylit), pairing native Latin-based orthography with automated, context-aware Amazigh script transliterations. It is built by processing raw text data through a rule-based algorithmic pipeline designed to enforce strict orthographic purity, manage contextual phonetic mutations, and isolate foreign vocabulary.
Dataset Details
Dataset Sources
- Repository: [More Information Needed]
- Source Project: Tatoeba Translation Corpus
Uses
Direct Use
- Training and fine-tuning machine translation models and sequence-to-sequence tokenizers.
- Evaluating local-first or low-resource script normalizers and converters.
Out-of-Scope Use
This dataset is strictly optimized for native phonetic structures. Text processing forcefully filters out unadapted loanwords, making it unsuited for generalized code-switching (e.g., raw French-Kabyle mixed text) that does not adhere to standard Amazigh structural frameworks.
Dataset Structure
The dataset contains three distinct configuration subsets formatted as Tab-Separated Values (.tsv) files with specific schemas:
1. Monolingual Subset (kab_tifinagh)
id: Unique identifier inherited from the Tatoeba database.kab_tfng: The processed Amazigh alphabet transliteration column.kab_latn: The original source Kabyle sentence in standard Latin script.
2. English Parallel Subset (en_kab_tifinagh)
id: Unique sentence identifier.kab_tfng: The processed Amazigh alphabet transliteration column.kab_latn: The source Kabyle sentence in standard Latin script.en: Corresponding English translation text string.
3. French Parallel Subset (fr_kab_tifinagh)
id: Unique sentence identifier.kab_tfng: The processed Amazigh alphabet transliteration column.kab_latn: The source Kabyle sentence in standard Latin script.fr: Corresponding French translation text string.
Dataset Creation
Curation Rationale
While the Kabyle language is widely written using a modified Latin alphabet, the standardized Amazigh alphabet holds deep official and cultural significance. This dataset bridges the script divide algorithmically, applying rigorous phonetic harmony and morphological adjustments to build a high-fidelity parallel repository.
Source Data
Data Collection and Processing
The source texts are extracted from the Kabyle subsets of the Tatoeba project. The pipeline maps characters directly to the 33 official IRCAM standardized alphabet characters while executing a multi-tier linguistic and structural engine:
1. Character Map & Realignment Graphemes
The pipeline uses a strict character matrix reducing entries directly to the core script block, explicitly accommodating gemination, labialization, and target replacements:
- Geminated Affricates:
čč=>ⵜⵜⵛ,ǧǧ=>ⴷⴷⵊ,žž=>ⴷⴷⵣ,ţţ=>ⵜⵜ. - Labialization Graphemes:
gʷ=>ⴳⵯ,kʷ=>ⴽⵯ. - Standard Alternations:
č=>ⵜⵛ,ǧ=>ⴷⵊ,ž=>ⴷⵣ,ţ=>ⵜⵜ. - Foreign Borrowing Adaptations: Maps validated morphological structures containing
p=>ⴱandv=>ⴼ.
2. Grammatical Dash Merging
Structural hyphens used for direct/indirect objects, prepositions, or kinship suffixes are automatically stripped, merging dependent particles cleanly into single word tokens:
- Prepositional/Object Frameworks:
fell-as=>ⴼⴻⵍⵍⴰⵙ,ger-aɣ=>ⴳⴻⵔⴰⵖ. - Locative/Locational Frameworks:
ɣur-sen=>ⵖⵓⵔⵙⴻⵏ,deg-sen=>ⴷⴻⴳⵙⴻⵏ. - Kinship Expressions:
baba-s=>ⴱⴰⴱⴰⵙ,mmi-k=>ⵎⵎⵉⴽ. - Note: Non-grammatical structural dashes are globally normalized to empty spaces.
3. Contextual Phonetics & Schwa (ⴻ) Optimization
- Initial Mutations: Word-initial sequences starting with
yeorwemigrate to native vocalic variantsiandurespectively. - Vocalic Semivowel Linearizations: Sequences matching
eworeyfollowed by a native consonant or sitting at the absolute end of a word are flattened directly intouori. - Pharyngealization Harmony: Progressive and regressive assimilation constraints force the plain alveolar trill
r/Rto upgrade into its pharyngealized counterpartṛ/Ṛif it directly borders or is separated by a single vowel from any pharyngeal anchor (ẓ,ḍ,ṭ,ṣ). - Schwa Stripping: The engine drops an initial schwa (
ⴻ) at the absolute start of a word. Internally,ⴻis dynamically dropped between two distinct consonants using a strict regex lookahead filter, but remains preserved when sandwiched between identical, geminated consonants.
4. Strict Isolation Filter (Skipped Sentences)
To prevent phonetic distortion, a row is completely discarded and sent to a _skipped.tsv ledger if a token meets any of the following conditions:
- Contains French accented characters (
é,è,â,ç, etc.). - Contains true diphthongs (doubled vowel sequences like
ae,ui,oi). - Contains the vowel
oorOunless it represents a structural character assignment or explicit native override. - Terminates explicitly with an unmapped trailing
eorE. - Contains an apostrophe (
') or represents an all-caps acronym (e.g.,MRI). - Contains standalone capital letters internally or at the tail end of strings.
- Fails a final structural purity regex check (
^[0-9ⵯ\u2D30-\u2D6F\s.,!?;:()""\']+$).
5. Target Overrides
High-frequency cross-linguistic tokens are explicitly caught by a manual override filter before any exclusion rules trigger:
Tom/ṭom/ṭum=>ⵟⵓⵎAit/ait=>ⴰⵢⵜMary=>ⵎⴰⵔⵉ
Who are the source data producers?
The underlying source corpus sentences are crowdsourced, verified, and translated by independent contributors and native speakers on the open-source Tatoeba platform.
Bias, Risks, and Limitations
Because the translation relies heavily on an automated algorithmic pipeline to catch external loanword structures, structural edge cases can occur:
- Loanwords that naturally lack explicit French accented markers, trailing
es, or common vowels likeomay slip past the isolation filters and undergo incorrect, literal transliteration into the Amazigh script. - Rare native structures or highly localized dialectal expressions matching an exclusion rule rule profile might be inadvertently skipped.
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