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Datasets:
KBLab
/
overlim

Tasks:
Text Classification
Modalities:
Tabular
Text
Sub-tasks:
natural-language-inference
semantic-similarity-classification
sentiment-classification
Languages:
Swedish
Danish
Norwegian Bokmål
Size:
1M - 10M
Tags:
qa-nli
paraphrase-identification
Libraries:
Datasets
License:
Dataset card Viewer Files Community
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  1. README.md +0 -178
  2. data/da/boolq.tar.gz → boolq_da/overlim-test.parquet +2 -2
  3. boolq_da/overlim-train.parquet +3 -0
  4. boolq_da/overlim-validation.parquet +3 -0
  5. boolq_nb/overlim-test.parquet +3 -0
  6. boolq_nb/overlim-train.parquet +3 -0
  7. boolq_nb/overlim-validation.parquet +3 -0
  8. boolq_sv/overlim-test.parquet +3 -0
  9. boolq_sv/overlim-train.parquet +3 -0
  10. boolq_sv/overlim-validation.parquet +3 -0
  11. data/da/wnli.tar.gz → cb_da/overlim-test.parquet +2 -2
  12. data/nb/cb.tar.gz → cb_da/overlim-train.parquet +2 -2
  13. data/da/cb.tar.gz → cb_da/overlim-validation.parquet +2 -2
  14. data/da/copa.tar.gz → cb_nb/overlim-test.parquet +2 -2
  15. cb_nb/overlim-train.parquet +3 -0
  16. cb_nb/overlim-validation.parquet +3 -0
  17. cb_sv/overlim-test.parquet +3 -0
  18. cb_sv/overlim-train.parquet +3 -0
  19. cb_sv/overlim-validation.parquet +3 -0
  20. copa_da/overlim-test.parquet +3 -0
  21. copa_da/overlim-train.parquet +3 -0
  22. copa_da/overlim-validation.parquet +3 -0
  23. copa_nb/overlim-test.parquet +3 -0
  24. copa_nb/overlim-train.parquet +3 -0
  25. copa_nb/overlim-validation.parquet +3 -0
  26. copa_sv/overlim-test.parquet +3 -0
  27. copa_sv/overlim-train.parquet +3 -0
  28. copa_sv/overlim-validation.parquet +3 -0
  29. data/da/qnli.tar.gz +0 -3
  30. data/da/qqp.tar.gz +0 -3
  31. data/da/sst.tar.gz +0 -3
  32. data/da/stsb.tar.gz +0 -3
  33. data/nb/boolq.tar.gz +0 -3
  34. data/nb/copa.tar.gz +0 -3
  35. data/nb/mnli.tar.gz +0 -3
  36. data/nb/mrpc.tar.gz +0 -3
  37. data/nb/qnli.tar.gz +0 -3
  38. data/nb/qqp.tar.gz +0 -3
  39. data/nb/rte.tar.gz +0 -3
  40. data/nb/sst.tar.gz +0 -3
  41. data/nb/stsb.tar.gz +0 -3
  42. data/nb/wnli.tar.gz +0 -3
  43. data/sv/boolq.tar.gz +0 -3
  44. data/sv/cb.tar.gz +0 -3
  45. data/sv/copa.tar.gz +0 -3
  46. data/sv/mnli.tar.gz +0 -3
  47. data/sv/mrpc.tar.gz +0 -3
  48. data/sv/qnli.tar.gz +0 -3
  49. data/sv/qqp.tar.gz +0 -3
  50. data/sv/rte.tar.gz +0 -3
README.md DELETED
@@ -1,178 +0,0 @@
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- ---
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- annotations_creators:
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- - other
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- language_creators:
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- - other
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- language:
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- - sv
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- - da
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- - nb
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- license:
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- - cc-by-4.0
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- multilinguality:
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- - translation
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- size_categories:
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- - unknown
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- source_datasets:
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- - extended|glue
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- - extended|super_glue
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- task_categories:
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- - text-classification
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- task_ids:
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- - natural-language-inference
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- - semantic-similarity-classification
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- - sentiment-classification
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- - text-scoring
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- pretty_name: overlim
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- tags:
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- - qa-nli
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- - paraphrase-identification
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- ---
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-
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- # Dataset Card for OverLim
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-
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- ## Dataset Description
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-
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- - **Homepage:**
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- - **Repository:**
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- - **Paper:**
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- - **Leaderboard:**
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- - **Point of Contact:**
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-
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- ### Dataset Summary
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-
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- The _OverLim_ dataset contains some of the GLUE and SuperGLUE tasks automatically
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- translated to Swedish, Danish, and Norwegian (bokmål), using the OpusMT models
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- for MarianMT.
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-
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- The translation quality was not manually checked and may thus be faulty.
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- Results on these datasets should thus be interpreted carefully.
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-
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- If you want to have an easy script to train and evaluate your models have a look [here](https://github.com/kb-labb/overlim_eval)
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-
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-
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- ### Supported Tasks and Leaderboards
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-
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- The data contains the following tasks from GLUE and SuperGLUE:
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-
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- - GLUE
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- - `mnli`
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- - `mrpc`
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- - `qnli`
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- - `qqp`
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- - `rte`
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- - `sst`
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- - `stsb`
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- - `wnli`
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- - SuperGLUE
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- - `boolq`
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- - `cb`
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- - `copa`
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- - `rte`
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-
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- ### Languages
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-
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- - Swedish
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- - Danish
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- - Norwegian (bokmål)
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-
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- ## Dataset Structure
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-
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- ### Data Instances
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-
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- Every task has their own set of features, but all share an `idx` and `label`.
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-
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- - GLUE
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- - `mnli`
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- - `premise`, `hypothesis`
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- - `mrpc`
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- - `text_a`, `text_b`
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- - `qnli`
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- - `premise`, `hypothesis`
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- - `qqp`
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- - `text_a`, `text_b`
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- - `sst`
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- - `text`
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- - `stsb`
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- - `text_a`, `text_b`
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- - `wnli`
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- - `premise`, `hypothesis`
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- - SuperGLUE
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- - `boolq`
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- - `question`, `passage`
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- - `cb`
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- - `premise`, `hypothesis`
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- - `copa`
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- - `premise`, `choice1`, `choice2`, `question`
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- - `rte`
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- - `premise`, `hypothesis`
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-
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- ### Data Splits
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-
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- In order to have test-split, we repurpose the original validation-split as
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- test-split, and split the training-split into a new training- and
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- validation-split, with an 80-20 distribution.
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-
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- ## Dataset Creation
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-
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- For more information about the individual tasks see (https://gluebenchmark.com) and (https://super.gluebenchmark.com).
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-
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- ### Curation Rationale
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-
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- Training non-English models is easy, but there is a lack of evaluation datasets to compare their actual performance.
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-
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- ### Source Data
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-
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- #### Initial Data Collection and Normalization
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-
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- [More Information Needed]
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-
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- #### Who are the source language producers?
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-
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- [More Information Needed]
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-
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- ### Annotations
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-
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- #### Annotation process
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-
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- [More Information Needed]
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-
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- #### Who are the annotators?
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-
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- [More Information Needed]
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-
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- ### Personal and Sensitive Information
145
-
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- [More Information Needed]
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-
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- ## Considerations for Using the Data
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-
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- ### Social Impact of Dataset
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-
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- [More Information Needed]
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-
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- ### Discussion of Biases
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-
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- [More Information Needed]
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-
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- ### Other Known Limitations
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-
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- [More Information Needed]
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-
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- ## Additional Information
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-
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- ### Dataset Curators
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-
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- [More Information Needed]
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-
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- ### Licensing Information
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-
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- [More Information Needed]
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-
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- ### Citation Information
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-
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- [More Information Needed]
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-
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- ### Contributions
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-
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- Thanks to [@kb-labb](https://github.com/kb-labb) for adding this dataset.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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