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qanastek
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51-languages-classifier

Text Classification
Transformers
PyTorch
Transformers
multi-class-classification
Inference Endpoints
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  1. .gitattributes +1 -0
  2. README.md +214 -1
  3. config.json +3 -0
  4. optimizer.pt +3 -0
  5. pytorch_model.bin +3 -0
  6. rng_state.pth +3 -0
  7. scheduler.pt +3 -0
  8. sentencepiece.bpe.model +3 -0
  9. special_tokens_map.json +3 -0
  10. tokenizer.json +3 -0
  11. tokenizer_config.json +3 -0
  12. trainer_state.json +3 -0
  13. training_args.bin +3 -0
.gitattributes CHANGED
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+ *.json filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -1,3 +1,216 @@
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  ---
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- license: cc-by-sa-4.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ tags:
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+ - Transformers
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+ - text-classification
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+ - multi-class-classification
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+ languages:
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+ - af-ZA
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+ - am-ET
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+ - ar-SA
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+ - az-AZ
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+ - bn-BD
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+ - cy-GB
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+ - da-DK
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+ - de-DE
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+ - el-GR
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+ - en-US
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+ - es-ES
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+ - fa-IR
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+ - fi-FI
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+ - fr-FR
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+ - he-IL
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+ - hi-IN
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+ - hu-HU
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+ - hy-AM
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+ - id-ID
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+ - is-IS
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+ - it-IT
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+ - ja-JP
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+ - jv-ID
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+ - ka-GE
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+ - km-KH
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+ - kn-IN
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+ - ko-KR
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+ - lv-LV
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+ - ml-IN
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+ - mn-MN
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+ - ms-MY
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+ - my-MM
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+ - nb-NO
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+ - nl-NL
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+ - pl-PL
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+ - pt-PT
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+ - ro-RO
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+ - ru-RU
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+ - sl-SL
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+ - sq-AL
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+ - sv-SE
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+ - sw-KE
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+ - ta-IN
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+ - te-IN
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+ - th-TH
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+ - tl-PH
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+ - tr-TR
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+ - ur-PK
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+ - vi-VN
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+ - zh-CN
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+ - zh-TW
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+ multilinguality:
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+ - af-ZA
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+ - am-ET
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+ - ar-SA
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+ - az-AZ
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+ - bn-BD
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+ - cy-GB
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+ - da-DK
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+ - de-DE
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+ - el-GR
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+ - en-US
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+ - es-ES
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+ - fa-IR
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+ - fi-FI
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+ - fr-FR
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+ - he-IL
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+ - hi-IN
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+ - hu-HU
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+ - hy-AM
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+ - id-ID
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+ - is-IS
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+ - it-IT
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+ - ja-JP
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+ - jv-ID
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+ - ka-GE
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+ - km-KH
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+ - kn-IN
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+ - ko-KR
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+ - lv-LV
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+ - ml-IN
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+ - mn-MN
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+ - ms-MY
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+ - my-MM
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+ - nb-NO
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+ - nl-NL
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+ - pl-PL
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+ - pt-PT
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+ - ro-RO
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+ - ru-RU
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+ - sl-SL
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+ - sq-AL
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+ - sv-SE
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+ - sw-KE
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+ - ta-IN
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+ - te-IN
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+ - th-TH
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+ - tl-PH
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+ - tr-TR
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+ - ur-PK
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+ - vi-VN
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+ - zh-CN
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+ - zh-TW
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+ datasets:
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+ - qanastek/MASSIVE
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+ widget:
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+ - text: "wake me up at five am this week"
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+ - text: "je veux écouter la chanson de jacques brel encore une fois"
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+ - text: "quiero escuchar la canción de arijit singh una vez más"
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+ - text: "olly onde é que á um parque por perto onde eu possa correr"
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+ - text: "פרק הבא בפודקאסט בבקשה"
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+ - text: "亚马逊股价"
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+ - text: "найди билет на поезд в санкт-петербург"
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+ license: cc-by-4.0
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  ---
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+
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+ **People Involved**
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+
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+ * [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1)
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+
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+ **Affiliations**
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+
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+ 1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France.
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+
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+ ## Demo: How to use in HuggingFace Transformers Pipeline
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+
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+ Requires [transformers](https://pypi.org/project/transformers/): ```pip install transformers```
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
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+ model_name = 'qanastek/51-languages-classifier'
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ model = AutoModelForSequenceClassification.from_pretrained(model_name)
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+ classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
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+ res = classifier("פרק הבא בפודקאסט בבקשה")
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+ print(res)
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+ ```
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+
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+ Outputs:
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+
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+ ```python
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+ [{'label': 'fr-FR', 'score': 0.9998375177383423}]
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+ ```
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+
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+ ## Training data
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+
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+ [MASSIVE](https://huggingface.co/datasets/qanastek/MASSIVE) is a parallel dataset of > 1M utterances across 51 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.
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+
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+
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+ ## Evaluation results
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+
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+ ```plain
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+ precision recall f1-score support
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+
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+ af-ZA 0.9821 0.9805 0.9813 2974
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+ am-ET 1.0000 1.0000 1.0000 2974
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+ ar-SA 0.9809 0.9822 0.9815 2974
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+ az-AZ 0.9946 0.9845 0.9895 2974
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+ bn-BD 0.9997 0.9990 0.9993 2974
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+ cy-GB 0.9970 0.9929 0.9949 2974
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+ da-DK 0.9575 0.9617 0.9596 2974
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+ de-DE 0.9906 0.9909 0.9908 2974
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+ el-GR 0.9997 0.9973 0.9985 2974
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+ en-US 0.9712 0.9866 0.9788 2974
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+ es-ES 0.9825 0.9842 0.9834 2974
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+ fa-IR 0.9940 0.9973 0.9956 2974
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+ fi-FI 0.9943 0.9946 0.9945 2974
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+ fr-FR 0.9963 0.9923 0.9943 2974
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+ he-IL 1.0000 0.9997 0.9998 2974
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+ hi-IN 1.0000 0.9980 0.9990 2974
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+ hu-HU 0.9983 0.9950 0.9966 2974
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+ hy-AM 1.0000 0.9993 0.9997 2974
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+ id-ID 0.9319 0.9291 0.9305 2974
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+ is-IS 0.9966 0.9943 0.9955 2974
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+ it-IT 0.9698 0.9926 0.9811 2974
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+ ja-JP 0.9987 0.9963 0.9975 2974
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+ jv-ID 0.9628 0.9744 0.9686 2974
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+ ka-GE 0.9993 0.9997 0.9995 2974
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+ km-KH 0.9867 0.9963 0.9915 2974
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+ kn-IN 1.0000 0.9993 0.9997 2974
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+ ko-KR 0.9917 0.9997 0.9956 2974
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+ lv-LV 0.9990 0.9950 0.9970 2974
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+ ml-IN 0.9997 0.9997 0.9997 2974
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+ mn-MN 0.9987 0.9966 0.9976 2974
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+ ms-MY 0.9359 0.9418 0.9388 2974
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+ my-MM 1.0000 0.9993 0.9997 2974
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+ nb-NO 0.9600 0.9533 0.9566 2974
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+ nl-NL 0.9850 0.9748 0.9799 2974
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+ pl-PL 0.9946 0.9923 0.9934 2974
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+ pt-PT 0.9885 0.9798 0.9841 2974
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+ ro-RO 0.9919 0.9916 0.9918 2974
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+ ru-RU 0.9976 0.9983 0.9980 2974
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+ sl-SL 0.9956 0.9939 0.9948 2974
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+ sq-AL 0.9936 0.9896 0.9916 2974
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+ sv-SE 0.9902 0.9842 0.9872 2974
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+ sw-KE 0.9867 0.9953 0.9910 2974
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+ ta-IN 1.0000 1.0000 1.0000 2974
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+ te-IN 1.0000 0.9997 0.9998 2974
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+ th-TH 1.0000 0.9983 0.9992 2974
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+ tl-PH 0.9929 0.9899 0.9914 2974
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+ tr-TR 0.9869 0.9872 0.9871 2974
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+ ur-PK 0.9983 0.9929 0.9956 2974
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+ vi-VN 0.9993 0.9973 0.9983 2974
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+ zh-CN 0.9812 0.9832 0.9822 2974
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+ zh-TW 0.9832 0.9815 0.9823 2974
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+
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+ accuracy 0.9889 151674
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+ macro avg 0.9889 0.9889 0.9889 151674
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+ weighted avg 0.9889 0.9889 0.9889 151674
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+ ```
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