Datasets:
File size: 9,346 Bytes
e2c27ad 77d807e e2c27ad 27111dc e2c27ad 27111dc baa01be e9bb494 e2c27ad 27111dc baa01be e9bb494 77d807e e2c27ad 77d807e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 |
---
annotations_creators:
- human-annotated
language:
- eng
- hau
- ibo
- pcm
- yor
license: cc-by-sa-4.0
multilinguality: multilingual
task_categories:
- translation
task_ids: []
dataset_info:
- config_name: en-ha
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 119594
num_examples: 410
download_size: 73989
dataset_size: 119594
- config_name: en-ig
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 120677
num_examples: 410
download_size: 71569
dataset_size: 120677
- config_name: en-pcm
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 113431
num_examples: 410
download_size: 74180
dataset_size: 113431
- config_name: en-yo
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 135511
num_examples: 410
download_size: 86308
dataset_size: 135511
configs:
- config_name: en-ha
data_files:
- split: train
path: en-ha/train-*
- config_name: en-ig
data_files:
- split: train
path: en-ig/train-*
- config_name: en-pcm
data_files:
- split: train
path: en-pcm/train-*
- config_name: en-yo
data_files:
- split: train
path: en-yo/train-*
tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<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;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">NollySentiBitextMining</h1>
<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>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>
NollySenti is Nollywood movie reviews for five languages widely spoken in Nigeria (English, Hausa, Igbo, Nigerian-Pidgin, and Yoruba.
| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Social, Reviews, Written |
| Reference | https://github.com/IyanuSh/NollySenti |
## How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
```python
import mteb
task = mteb.get_tasks(["NollySentiBitextMining"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```
<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
## Citation
If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
```bibtex
@inproceedings{shode2023nollysenti,
author = {Shode, Iyanuoluwa and Adelani, David Ifeoluwa and Peng, Jing and Feldman, Anna},
booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
pages = {986--998},
title = {NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification},
year = {2023},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```
# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>
The following code contains the descriptive statistics from the task. These can also be obtained using:
```python
import mteb
task = mteb.get_task("NollySentiBitextMining")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"train": {
"num_samples": 1640,
"number_of_characters": 445805,
"unique_pairs": 1632,
"min_sentence1_length": 3,
"average_sentence1_length": 136.3170731707317,
"max_sentence1_length": 1698,
"unique_sentence1": 405,
"min_sentence2_length": 3,
"average_sentence2_length": 135.515243902439,
"max_sentence2_length": 1728,
"unique_sentence2": 1631,
"hf_subset_descriptive_stats": {
"en-ha": {
"num_samples": 410,
"number_of_characters": 115348,
"unique_pairs": 407,
"min_sentence1_length": 3,
"average_sentence1_length": 136.3170731707317,
"max_sentence1_length": 1698,
"unique_sentence1": 405,
"min_sentence2_length": 4,
"average_sentence2_length": 145.01951219512196,
"max_sentence2_length": 1728,
"unique_sentence2": 407
},
"en-ig": {
"num_samples": 410,
"number_of_characters": 107173,
"unique_pairs": 409,
"min_sentence1_length": 3,
"average_sentence1_length": 136.3170731707317,
"max_sentence1_length": 1698,
"unique_sentence1": 405,
"min_sentence2_length": 5,
"average_sentence2_length": 125.08048780487805,
"max_sentence2_length": 1137,
"unique_sentence2": 408
},
"en-pcm": {
"num_samples": 410,
"number_of_characters": 109955,
"unique_pairs": 408,
"min_sentence1_length": 3,
"average_sentence1_length": 136.3170731707317,
"max_sentence1_length": 1698,
"unique_sentence1": 405,
"min_sentence2_length": 3,
"average_sentence2_length": 131.8658536585366,
"max_sentence2_length": 1552,
"unique_sentence2": 408
},
"en-yo": {
"num_samples": 410,
"number_of_characters": 113329,
"unique_pairs": 409,
"min_sentence1_length": 3,
"average_sentence1_length": 136.3170731707317,
"max_sentence1_length": 1698,
"unique_sentence1": 405,
"min_sentence2_length": 6,
"average_sentence2_length": 140.0951219512195,
"max_sentence2_length": 1338,
"unique_sentence2": 409
}
}
}
}
```
</details>
---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)* |