Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
semantic-similarity-classification
Size:
10K - 100K
ArXiv:
License:
Add dataset card
Browse files
README.md
CHANGED
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- rus
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license: unknown
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multilinguality: translated
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task_ids: []
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task_categories:
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- text-classification
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tags:
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- mteb
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- text
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- Fiction
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- Government
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- Written
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dataset_info:
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- config_name: assamese
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features:
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- name: sentence1
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dtype: string
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dtype: string
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dtype: int64
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- name: test
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num_bytes: 565556
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num_examples: 1365
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download_size: 232509
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dataset_size: 565556
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features:
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num_bytes: 567227
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num_examples: 1365
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download_size: 224982
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dataset_size: 567227
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features:
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num_bytes: 549145
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num_examples: 1365
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download_size: 221829
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dataset_size: 549145
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dataset_size: 598335
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dataset_size: 676943
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download_size: 157342
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dataset_size: 246707
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configs:
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- config_name: assamese
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data_files:
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path: assamese/test-*
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path: turkish/test-*
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---
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<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
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-
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This is subset of 'XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding' with languages that were not part of the original XNLI plus three (verified) languages that are not strongly covered in MTEB
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-
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- Task category: t2t
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- Domains: ['Non-fiction', 'Fiction', 'Government', 'Written']
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## How to evaluate on this task
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```python
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import mteb
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@@ -288,7 +61,7 @@ evaluator.run(model)
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```
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<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
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-
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## Citation
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@@ -327,6 +100,19 @@ If you use this dataset, please cite the dataset as well as [mteb](https://githu
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```
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# Dataset Statistics
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```json
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{
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"test": {
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}
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}
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}
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```
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- rus
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license: unknown
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multilinguality: translated
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task_categories:
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- text-classification
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+
task_ids: []
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tags:
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- mteb
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- text
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- Fiction
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- Government
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- Written
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---
|
| 32 |
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
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+
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<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
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<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">XNLIV2</h1>
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<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
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<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
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+
</div>
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This is subset of 'XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding' with languages that were not part of the original XNLI plus three (verified) languages that are not strongly covered in MTEB
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| | |
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|----------------|---------------------------------------------|
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| Task category: | t2t |
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| Domains | ['Non-fiction', 'Fiction', 'Government', 'Written'] |
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| Reference | https://arxiv.org/pdf/2301.06527 |
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## How to evaluate on this task
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| 50 |
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| 51 |
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You can evaluate an embedding model on this dataset using the following code:
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+
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```python
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| 54 |
import mteb
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```
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<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
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To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
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## Citation
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| 67 |
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| 100 |
```
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| 102 |
# Dataset Statistics
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| 103 |
+
<details>
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| 104 |
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<summary> Dataset Statistics</summary>
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| 105 |
+
|
| 106 |
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The following code contains the descriptive statistics from the task. These can also be obtained using:
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| 107 |
+
|
| 108 |
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```python
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| 109 |
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import mteb
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| 110 |
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| 111 |
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task = mteb.get_task("XNLIV2")
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| 112 |
+
|
| 113 |
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desc_stats = task.metadata.descriptive_stats
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| 114 |
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```
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| 115 |
+
|
| 116 |
```json
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| 117 |
{
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| 118 |
"test": {
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|
| 426 |
}
|
| 427 |
}
|
| 428 |
}
|
| 429 |
+
```
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| 430 |
+
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| 431 |
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</details>
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| 432 |
+
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| 433 |
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---
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*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*
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