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Datasets:
mteb
/
mlsum

Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
topic-classification
Languages:
German
French
Russian
Size:
1M - 10M
ArXiv:
Tags:
mteb
text
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files
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Community
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  1. README.md +29 -785
README.md CHANGED
@@ -11,7 +11,7 @@ multilinguality: multilingual
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  source_datasets:
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  - mteb/mlsum
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  task_categories:
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- - text-clustering
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  task_ids:
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  - topic-classification
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  dataset_info:
@@ -193,7 +193,7 @@ tags:
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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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  <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;">MLSUMClusteringS2S.v2</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>
@@ -214,7 +214,7 @@ You can evaluate an embedding model on this dataset using the following code:
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  ```python
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  import mteb
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- task = mteb.get_tasks(["MLSUMClusteringS2S.v2"])
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  evaluator = mteb.MTEB(task)
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  model = mteb.get_model(YOUR_MODEL)
@@ -268,776 +268,20 @@ The following code contains the descriptive statistics from the task. These can
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  ```python
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  import mteb
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- task = mteb.get_task("MLSUMClusteringS2S.v2")
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  desc_stats = task.metadata.descriptive_stats
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  ```
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  ```json
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- "11": {
1025
- "count": 1
1026
- },
1027
- "6": {
1028
- "count": 1
1029
- }
1030
- }
1031
- }
1032
- }
1033
- },
1034
  "test": {
1035
  "num_samples": 6900,
1036
- "number_of_characters": 30705479,
1037
- "min_text_length": 288,
1038
- "average_text_length": 4450.069420289855,
1039
- "max_text_length": 135921,
1040
- "unique_texts": 4336,
1041
  "min_labels_per_text": 1,
1042
  "average_labels_per_text": 1.0,
1043
  "max_labels_per_text": 1040,
@@ -1287,11 +531,11 @@ desc_stats = task.metadata.descriptive_stats
1287
  "hf_subset_descriptive_stats": {
1288
  "de": {
1289
  "num_samples": 2048,
1290
- "number_of_characters": 8183989,
1291
- "min_text_length": 660,
1292
- "average_text_length": 3996.08837890625,
1293
- "max_text_length": 25967,
1294
- "unique_texts": 1715,
1295
  "min_labels_per_text": 20,
1296
  "average_labels_per_text": 1.0,
1297
  "max_labels_per_text": 692,
@@ -1337,11 +581,11 @@ desc_stats = task.metadata.descriptive_stats
1337
  },
1338
  "fr": {
1339
  "num_samples": 2048,
1340
- "number_of_characters": 7735699,
1341
- "min_text_length": 291,
1342
- "average_text_length": 3777.19677734375,
1343
- "max_text_length": 50088,
1344
- "unique_texts": 1524,
1345
  "min_labels_per_text": 1,
1346
  "average_labels_per_text": 1.0,
1347
  "max_labels_per_text": 333,
@@ -1534,11 +778,11 @@ desc_stats = task.metadata.descriptive_stats
1534
  },
1535
  "ru": {
1536
  "num_samples": 756,
1537
- "number_of_characters": 5128031,
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- "min_text_length": 395,
1539
- "average_text_length": 6783.109788359789,
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- "max_text_length": 135921,
1541
- "unique_texts": 732,
1542
  "min_labels_per_text": 15,
1543
  "average_labels_per_text": 1.0,
1544
  "max_labels_per_text": 203,
@@ -1575,11 +819,11 @@ desc_stats = task.metadata.descriptive_stats
1575
  },
1576
  "es": {
1577
  "num_samples": 2048,
1578
- "number_of_characters": 9657760,
1579
- "min_text_length": 288,
1580
- "average_text_length": 4715.703125,
1581
- "max_text_length": 85710,
1582
- "unique_texts": 1785,
1583
  "min_labels_per_text": 1,
1584
  "average_labels_per_text": 1.0,
1585
  "max_labels_per_text": 228,
 
11
  source_datasets:
12
  - mteb/mlsum
13
  task_categories:
14
+ - text-classification
15
  task_ids:
16
  - topic-classification
17
  dataset_info:
 
193
  <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
194
 
195
  <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;">
196
+ <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">MLSUMClusteringP2P.v2</h1>
197
  <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>
198
  <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
199
  </div>
 
214
  ```python
215
  import mteb
216
 
217
+ task = mteb.get_tasks(["MLSUMClusteringP2P.v2"])
218
  evaluator = mteb.MTEB(task)
219
 
220
  model = mteb.get_model(YOUR_MODEL)
 
268
  ```python
269
  import mteb
270
 
271
+ task = mteb.get_task("MLSUMClusteringP2P.v2")
272
 
273
  desc_stats = task.metadata.descriptive_stats
274
  ```
275
 
276
  ```json
277
  {
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
278
  "test": {
279
  "num_samples": 6900,
280
+ "number_of_characters": 31106070,
281
+ "min_text_length": 296,
282
+ "average_text_length": 4508.126086956522,
283
+ "max_text_length": 135984,
284
+ "unique_texts": 4373,
285
  "min_labels_per_text": 1,
286
  "average_labels_per_text": 1.0,
287
  "max_labels_per_text": 1040,
 
531
  "hf_subset_descriptive_stats": {
532
  "de": {
533
  "num_samples": 2048,
534
+ "number_of_characters": 8267329,
535
+ "min_text_length": 693,
536
+ "average_text_length": 4036.78173828125,
537
+ "max_text_length": 26023,
538
+ "unique_texts": 1731,
539
  "min_labels_per_text": 20,
540
  "average_labels_per_text": 1.0,
541
  "max_labels_per_text": 692,
 
581
  },
582
  "fr": {
583
  "num_samples": 2048,
584
+ "number_of_characters": 7885898,
585
+ "min_text_length": 359,
586
+ "average_text_length": 3850.5361328125,
587
+ "max_text_length": 50130,
588
+ "unique_texts": 1514,
589
  "min_labels_per_text": 1,
590
  "average_labels_per_text": 1.0,
591
  "max_labels_per_text": 333,
 
778
  },
779
  "ru": {
780
  "num_samples": 756,
781
+ "number_of_characters": 5174721,
782
+ "min_text_length": 444,
783
+ "average_text_length": 6844.869047619048,
784
+ "max_text_length": 135984,
785
+ "unique_texts": 720,
786
  "min_labels_per_text": 15,
787
  "average_labels_per_text": 1.0,
788
  "max_labels_per_text": 203,
 
819
  },
820
  "es": {
821
  "num_samples": 2048,
822
+ "number_of_characters": 9778122,
823
+ "min_text_length": 296,
824
+ "average_text_length": 4774.4736328125,
825
+ "max_text_length": 85821,
826
+ "unique_texts": 1784,
827
  "min_labels_per_text": 1,
828
  "average_labels_per_text": 1.0,
829
  "max_labels_per_text": 228,