xomad/gliner-model-merge-large-v1.0
Token Classification
•
Updated
•
4.21k
•
17
id
stringlengths 1
4
| sample_id
stringlengths 36
36
| tokens
sequence | ner_tags
sequence | ner_tags_index
sequence |
---|---|---|---|---|
0 | 7db90724-a10b-4e6c-b95e-355d051a59bf | [
"স্টেশনটি",
"প্ল্যাটফর্ম",
"স্ক্রিন",
"ডোর",
"দিয়ে",
"সজ্জিত।"
] | [
"O",
"B-OtherPROD",
"I-OtherPROD",
"I-OtherPROD",
"O",
"O"
] | [
0,
61,
62,
62,
0,
0
] |
1 | ec02ea49-d32e-4e6f-a634-f0e15c7b8301 | [
"উদ্ভিদটির",
"৯০",
"০০০",
"এরও",
"বেশি",
"সৌর",
"প্যানেল",
"২৩৫",
"একরও",
"বেশি",
"রয়েছে।"
] | [
"O",
"O",
"O",
"O",
"O",
"B-OtherPROD",
"I-OtherPROD",
"O",
"O",
"O",
"O"
] | [
0,
0,
0,
0,
0,
61,
62,
0,
0,
0,
0
] |
2 | e6507a5f-a3f9-49ff-a2b8-06add8fb5348 | [
"এই",
"মিশনটি",
"চন্দ্র",
"এক্স-রশ্মি",
"মানমন্দির",
"মোতায়েন",
"করেছে।"
] | [
"O",
"O",
"B-OtherPROD",
"I-OtherPROD",
"I-OtherPROD",
"O",
"O"
] | [
0,
0,
61,
62,
62,
0,
0
] |
3 | e5779e4e-a2e6-47ad-a9aa-a9b5c3458fbd | [
"সংস্থাটি",
"বেশ",
"কয়েকটি",
"বিমান",
"এবং",
"রাডার",
"স্কোয়াড্রনগুলিতে",
"কমান্ড",
"এবং",
"নিয়ন্ত্রণ",
"সরবরাহ",
"করেছে।"
] | [
"O",
"O",
"O",
"O",
"O",
"B-OtherPROD",
"O",
"O",
"O",
"O",
"O",
"O"
] | [
0,
0,
0,
0,
0,
61,
0,
0,
0,
0,
0,
0
] |
4 | 0cef1625-55a9-4e0f-8998-157a06113ff8 | [
"ট্রাম",
"স্টেশনটি",
"কোচি",
"কোওচি",
"প্রশাসনিক",
"অঞ্চল",
"সূর্য",
"উদয়ের",
"দেশ",
"।"
] | [
"B-OtherPROD",
"O",
"B-HumanSettlement",
"B-ORG",
"I-ORG",
"I-ORG",
"B-HumanSettlement",
"I-HumanSettlement",
"I-HumanSettlement",
"O"
] | [
61,
0,
5,
37,
38,
38,
5,
6,
6,
0
] |
5 | e38f7336-5151-4dea-93bf-736b08b26c78 | [
"একটি",
"কোয়ান্টাম",
"পরিগণনা",
"এমন",
"একটি",
"ডিভাইস",
"যা",
"গণনার",
"জন্য",
"কোয়ান্টাম",
"প্রক্রিয়া",
"ব্যবহার",
"করে।"
] | [
"O",
"B-OtherPROD",
"I-OtherPROD",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] | [
0,
61,
62,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
] |
6 | ea3ea470-b4ed-429a-a75c-e9524196b6eb | [
"এটি",
"৭.৬২×৫১মিমি",
"ন্যাটো",
"কার্তুজ",
"ব্যবহার",
"করে",
"তবে",
"ব্যারেল",
"বৈকল্পিকের",
"উপর",
"নির্ভর",
"করে",
"অন্যান্য",
"বেশ",
"কয়েকটি",
"কার্তুজের",
"জন্যও",
"চেম্বার",
"করা",
"যেতে",
"পারে।"
] | [
"O",
"B-OtherPROD",
"I-OtherPROD",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] | [
0,
61,
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0,
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0,
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0,
0,
0,
0,
0,
0,
0,
0,
0,
0
] |
7 | e958ed29-6c6e-4af5-8d60-87700dc6a106 | [
"সুপারসোনিক",
"প্ল্যাটফর্মে",
"একই",
"বৈধতা",
"দেওয়ার",
"জন্য",
"মোডগুলি",
"একটি",
"ব্যবসায়িক",
"জেট",
"বোর্ডে",
"বৈধ",
"করা",
"হয়েছে",
"এবং",
"আরও",
"প্রুফিং",
"হাল",
"তেজস",
"এ",
"করা",
"হচ্ছে।"
] | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-OtherPROD",
"I-OtherPROD",
"O",
"O",
"O"
] | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
61,
62,
0,
0,
0
] |
8 | 29d1c5bb-2076-4dbb-b630-8b2d438c5823 | [
"সমালোচকরা",
"নীতিটিকে",
"প্রতিরক্ষামূলক",
"হিসাবে",
"দেখেন",
"এবং",
"বড়",
"ব্যাংক",
"বা",
"অন্যান্য",
"প্রতিষ্ঠানগুলি",
"যদি",
"তাদের",
"ঝুঁকি",
"ব্যবস্থাপনা",
"কার্যকর",
"না",
"হয়",
"তবে",
"ব্যর্থ",
"হতে",
"হবে।"
] | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-OtherPROD",
"I-OtherPROD",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] | [
0,
0,
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0,
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0,
0,
61,
62,
0,
0,
0,
0,
0,
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0
] |
9 | d959dbf5-7534-4d42-9e7f-23cc9bec5569 | [
"দুজনের",
"মধ্যে",
"একটি",
"ইমেলের",
"মধ্যে",
"তারা",
"অস্ত্রের",
"বিনিময়",
"হার",
"এবং",
"দামগুলি",
"নিয়ে",
"আলোচনা",
"করেছে",
"বিশেষত",
"একটি",
"এম৬০",
"।"
] | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-OtherPROD",
"O"
] | [
0,
0,
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0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
61,
0
] |
10 | 6a517927-cea6-4519-b93d-489705a04efd | [
"অন্যান্য",
"ভাড়াটেদের",
"মধ্যে",
"ইন্টারন্যাশনাল",
"বিজনেস",
"মেশিন",
"কোং",
"সহায়ক",
"সংস্থা",
"রেড",
"হ্যাট",
"আইন",
"সংস্থাগুলি",
"এবং",
"আর্থিক",
"সংস্থাগুলি",
"অন্তর্ভুক্ত",
"রয়েছে।"
] | [
"O",
"O",
"O",
"B-OtherPROD",
"I-OtherPROD",
"I-OtherPROD",
"I-OtherPROD",
"O",
"O",
"B-PublicCorp",
"I-PublicCorp",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] | [
0,
0,
0,
61,
62,
62,
62,
0,
0,
23,
24,
0,
0,
0,
0,
0,
0,
0
] |
11 | 289b6c60-150b-49e9-8572-7e9f6e1daa92 | [
"ইন্টেল",
"কর্পোরেশন",
"৪",
"বিট",
"প্রসেসর",
"ইন্টেল",
"৪০০৪",
"মেমরি",
"স্ট্যাকের",
"চেয়ে",
"অভ্যন্তরীণ",
"স্ট্যাক",
"প্রয়োগ",
"করে।",
")"
] | [
"B-PublicCorp",
"I-PublicCorp",
"O",
"O",
"O",
"B-OtherPROD",
"I-OtherPROD",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] | [
23,
24,
0,
0,
0,
61,
62,
0,
0,
0,
0,
0,
0,
0,
0
] |
12 | eeec0e2a-6d28-4814-bfe4-2cf7e12db237 | [
"ঘন",
"কুয়াশা",
"এই",
"অঞ্চলটি",
"কম্বল",
"করে",
"এবং",
"তার",
"অন্ধ",
"ছেড়ে",
"চলে",
"গেছে",
"যখন",
"তার",
"রাডার",
"কমিশনের",
"বাইরে",
"ছিল।"
] | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-OtherPROD",
"O",
"O",
"O"
] | [
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0,
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61,
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0
] |
13 | ed63d952-fc60-4d6a-9bd1-ceea994847f7 | [
"অ্যাপল",
"থেকে",
"ম্যাকবুক",
"প্রো",
"এর",
"অনুরূপ",
"নকশার",
"সাথে",
"এটি",
"একটি",
"লিনাক্স",
"অপারেটিং",
"সিস্টেমটি",
"ডিফল্টরূপে",
"বহন",
"করবে।"
] | [
"B-PublicCorp",
"O",
"B-OtherPROD",
"I-OtherPROD",
"O",
"O",
"O",
"O",
"O",
"O",
"B-Software",
"O",
"O",
"O",
"O",
"O"
] | [
23,
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61,
62,
0,
0,
0,
0,
0,
0,
17,
0,
0,
0,
0,
0
] |
14 | 24f0049e-22f2-4741-aa59-12833c9aad96 | [
"আইপ্যাড",
"এই",
"প্রবণতার",
"সর্বাধিক",
"সুস্পষ্ট",
"উদাহরণ",
"তবে",
"মোবাইল",
"ফোনগুলিও",
"ই",
"রিডিং",
"সফ্টওয়্যার",
"হোস্ট",
"করতে",
"পারে।"
] | [
"B-OtherPROD",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] | [
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0,
0,
0,
0,
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0,
0,
0,
0
] |
15 | c2e6d15c-a21e-4667-8bad-93a6bdb0e3b0 | [
"মিমবর",
"এর",
"ক্ষেত্রটি",
"৩",
"×",
"১",
".",
"৩৮",
"মিটার",
"রয়েছে।"
] | [
"B-OtherPROD",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] | [
61,
0,
0,
0,
0,
0,
0,
0,
0,
0
] |
16 | 57dfddab-94bf-4f8c-8495-5466d6ead0e0 | [
"জ্বলন্ত",
"উপর",
"একটি",
"প্রকরণ",
"হ'ল",
"ইথানল",
"এর",
"৭০",
"%",
"বা",
"আরও",
"বেশি",
"ঘন",
"দ্রবণে",
"অবজেক্টটি",
"ডুবানো",
"তারপরে",
"সংক্ষিপ্তভাবে",
"একটি",
"বুনসেন",
"বার্নার",
"শিখায়",
"বস্তুটি",
"স্পর্শ",
"করুন।"
] | [
"O",
"O",
"O",
"O",
"O",
"B-Medication/Vaccine",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-OtherPROD",
"I-OtherPROD",
"O",
"O",
"O",
"O"
] | [
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] |
17 | 8e675bbe-1ad0-44f2-8427-2a0e82738913 | [
"স্ট্র্যাপ",
"একটি",
"হাতবন্দুক",
"এর",
"জন্য",
"একটি",
"কথোপকথন",
"শব্দ"
] | [
"O",
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"B-OtherPROD",
"O",
"O",
"O",
"O",
"O"
] | [
0,
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61,
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] |
18 | 340dc9e9-af37-4784-aebd-9b81ae6ff783 | [
"তিনি",
"কর্মীদের",
"দ্বারা",
"কুস্তিগীর",
"না",
"হওয়া",
"পর্যন্ত",
"তিনি",
"রান্নাঘরের",
"ছুরি",
"দিয়ে",
"শিক্ষার্থী",
"এবং",
"শিক্ষকদের",
"আক্রমণ",
"করতে",
"শুরু",
"করেছিলেন।"
] | [
"O",
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"O",
"O",
"O",
"O",
"O",
"B-OtherPROD",
"I-OtherPROD",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] | [
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61,
62,
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0,
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0
] |
19 | d3887b33-6fad-4058-a769-c82c97bcc754 | [
"সাইটটি",
"প্রাথমিকভাবে",
"দুটি",
"ফ্যালকন",
"হেভি",
"লঞ্চ",
"সহ",
"প্রতি",
"বছর",
"১২",
"টি",
"বাণিজ্যিক",
"লঞ্চ",
"সমর্থন",
"করবে।"
] | [
"O",
"O",
"O",
"B-OtherPROD",
"I-OtherPROD",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] | [
0,
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61,
62,
0,
0,
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0,
0,
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0,
0,
0
] |
20 | b658f0b3-eac5-4f04-9e40-368816f00826 | [
"কক্ষপথ",
"থেকে",
"কার্গো",
"নামিয়ে",
"আনতে",
"সক্ষম",
"কেবলমাত্র",
"অন্যান্য",
"পুনরায়",
"সাপ্লাই",
"মহাকাশযান",
"হ'ল",
"স্পেস",
"এক্স",
"স্পেস",
"এক্স",
"ড্রাগন",
"২",
"কার্গো",
"মহাকাশযান।"
] | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-AerospaceManufacturer",
"I-AerospaceManufacturer",
"B-OtherPROD",
"I-OtherPROD",
"I-OtherPROD",
"I-OtherPROD",
"O",
"O"
] | [
0,
0,
0,
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90 | 1a3544f0-02b0-48e8-be00-1ae6e8813762 | [
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92 | 69ee6eaa-0ba9-4c2e-86ca-6bec3fdfc576 | [
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93 | 7b311534-fae2-4712-a57d-dbcf1f21a95c | [
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94 | 306baece-eeb9-43c7-89f2-fff1c4153d3f | [
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95 | de22f78d-8d7f-40fc-860a-a7fa69832d55 | [
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96 | 2efdd870-622b-41c4-a695-53d9b08f3fb6 | [
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97 | 6cf67b53-b75d-4854-9f5b-537081f12f25 | [
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98 | 119137fe-0d24-452a-82d2-24f6108f6090 | [
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")",
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The tagset of MultiCoNER is a fine-grained tagset. The fine to coarse level mapping of the tags are as follows:
The final leaderboard of the shared task is available here.
Supported languages are Bangla, Chinese, English, Spanish, Farsi, French, German, Hindi, Italian, Portuguese, Swedish, Ukrainian.
The dataset follows CoNLL format.
Here are some examples in different languages:
The data has two fields. One is the token and another is the label. Here is an example from the English data.
# id f5458a3a-cd23-4df4-8384-4e23fe33a66b domain=en
doris _ _ B-Artist
day _ _ I-Artist
included _ _ O
in _ _ O
the _ _ O
album _ _ O
billy _ _ B-MusicalWork
rose _ _ I-MusicalWork
's _ _ I-MusicalWork
jumbo _ _ I-MusicalWork
Train, Dev, and Test splits are provided
TBD
from datasets import load_dataset
english_data = load_dataset('MultiCoNER/multiconer_v2', 'English (EN)')
CC BY 4.0
@inproceedings{multiconer2-report,
title={{SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)}},
author={Fetahu, Besnik and Kar, Sudipta and Chen, Zhiyu and Rokhlenko, Oleg and Malmasi, Shervin},
booktitle={Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)},
year={2023},
publisher={Association for Computational Linguistics},
}
@article{multiconer2-data,
title={{MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition}},
author={Fetahu, Besnik and Chen, Zhiyu and Kar, Sudipta and Rokhlenko, Oleg and Malmasi, Shervin},
year={2023},
}