Datasets:

Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
ArXiv:
DOI:
Libraries:
Datasets
Dask
License:
File size: 23,160 Bytes
dad5cfb
 
 
9248047
dad5cfb
9248047
 
dad5cfb
9248047
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
dad5cfb
 
 
 
 
 
 
6861e17
dad5cfb
 
 
20b2c9f
dad5cfb
 
 
 
 
75fa67c
 
3c452cb
75fa67c
dad5cfb
 
6861e17
dad5cfb
 
 
 
6861e17
dad5cfb
9248047
 
 
dad5cfb
9248047
dad5cfb
 
 
 
 
 
 
9248047
dad5cfb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
30cbcd6
dad5cfb
 
 
 
 
 
ede1fa7
dad5cfb
ede1fa7
dad5cfb
 
 
 
22b0aca
dad5cfb
ede1fa7
ab8f77d
dad5cfb
ede1fa7
dad5cfb
8cb2cd5
ede1fa7
 
dad5cfb
8cb2cd5
dad5cfb
ede1fa7
dad5cfb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5b89d1e
dad5cfb
 
 
 
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
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
---
license: odc-by
task_categories:
  - text-generation
language:
  - en
pretty_name: FineWeb-Edu
size_categories:
  - n>1T
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*/*
  - config_name: sample-10BT
    data_files:
      - split: train
        path: sample/10BT/*
  - config_name: sample-100BT
    data_files:
      - split: train
        path: sample/100BT/*
  - config_name: sample-350BT
    data_files:
      - split: train
        path: sample/350BT/*
  - config_name: CC-MAIN-2024-10
    data_files:
      - split: train
        path: data/CC-MAIN-2024-10/*
  - config_name: CC-MAIN-2023-50
    data_files:
      - split: train
        path: data/CC-MAIN-2023-50/*
  - config_name: CC-MAIN-2023-40
    data_files:
      - split: train
        path: data/CC-MAIN-2023-40/*
  - config_name: CC-MAIN-2023-23
    data_files:
      - split: train
        path: data/CC-MAIN-2023-23/*
  - config_name: CC-MAIN-2023-14
    data_files:
      - split: train
        path: data/CC-MAIN-2023-14/*
  - config_name: CC-MAIN-2023-06
    data_files:
      - split: train
        path: data/CC-MAIN-2023-06/*
  - config_name: CC-MAIN-2022-49
    data_files:
      - split: train
        path: data/CC-MAIN-2022-49/*
  - config_name: CC-MAIN-2022-40
    data_files:
      - split: train
        path: data/CC-MAIN-2022-40/*
  - config_name: CC-MAIN-2022-33
    data_files:
      - split: train
        path: data/CC-MAIN-2022-33/*
  - config_name: CC-MAIN-2022-27
    data_files:
      - split: train
        path: data/CC-MAIN-2022-27/*
  - config_name: CC-MAIN-2022-21
    data_files:
      - split: train
        path: data/CC-MAIN-2022-21/*
  - config_name: CC-MAIN-2022-05
    data_files:
      - split: train
        path: data/CC-MAIN-2022-05/*
  - config_name: CC-MAIN-2021-49
    data_files:
      - split: train
        path: data/CC-MAIN-2021-49/*
  - config_name: CC-MAIN-2021-43
    data_files:
      - split: train
        path: data/CC-MAIN-2021-43/*
  - config_name: CC-MAIN-2021-39
    data_files:
      - split: train
        path: data/CC-MAIN-2021-39/*
  - config_name: CC-MAIN-2021-31
    data_files:
      - split: train
        path: data/CC-MAIN-2021-31/*
  - config_name: CC-MAIN-2021-25
    data_files:
      - split: train
        path: data/CC-MAIN-2021-25/*
  - config_name: CC-MAIN-2021-21
    data_files:
      - split: train
        path: data/CC-MAIN-2021-21/*
  - config_name: CC-MAIN-2021-17
    data_files:
      - split: train
        path: data/CC-MAIN-2021-17/*
  - config_name: CC-MAIN-2021-10
    data_files:
      - split: train
        path: data/CC-MAIN-2021-10/*
  - config_name: CC-MAIN-2021-04
    data_files:
      - split: train
        path: data/CC-MAIN-2021-04/*
  - config_name: CC-MAIN-2020-50
    data_files:
      - split: train
        path: data/CC-MAIN-2020-50/*
  - config_name: CC-MAIN-2020-45
    data_files:
      - split: train
        path: data/CC-MAIN-2020-45/*
  - config_name: CC-MAIN-2020-40
    data_files:
      - split: train
        path: data/CC-MAIN-2020-40/*
  - config_name: CC-MAIN-2020-34
    data_files:
      - split: train
        path: data/CC-MAIN-2020-34/*
  - config_name: CC-MAIN-2020-29
    data_files:
      - split: train
        path: data/CC-MAIN-2020-29/*
  - config_name: CC-MAIN-2020-24
    data_files:
      - split: train
        path: data/CC-MAIN-2020-24/*
  - config_name: CC-MAIN-2020-16
    data_files:
      - split: train
        path: data/CC-MAIN-2020-16/*
  - config_name: CC-MAIN-2020-10
    data_files:
      - split: train
        path: data/CC-MAIN-2020-10/*
  - config_name: CC-MAIN-2020-05
    data_files:
      - split: train
        path: data/CC-MAIN-2020-05/*
  - config_name: CC-MAIN-2019-51
    data_files:
      - split: train
        path: data/CC-MAIN-2019-51/*
  - config_name: CC-MAIN-2019-47
    data_files:
      - split: train
        path: data/CC-MAIN-2019-47/*
  - config_name: CC-MAIN-2019-43
    data_files:
      - split: train
        path: data/CC-MAIN-2019-43/*
  - config_name: CC-MAIN-2019-39
    data_files:
      - split: train
        path: data/CC-MAIN-2019-39/*
  - config_name: CC-MAIN-2019-35
    data_files:
      - split: train
        path: data/CC-MAIN-2019-35/*
  - config_name: CC-MAIN-2019-30
    data_files:
      - split: train
        path: data/CC-MAIN-2019-30/*
  - config_name: CC-MAIN-2019-26
    data_files:
      - split: train
        path: data/CC-MAIN-2019-26/*
  - config_name: CC-MAIN-2019-22
    data_files:
      - split: train
        path: data/CC-MAIN-2019-22/*
  - config_name: CC-MAIN-2019-18
    data_files:
      - split: train
        path: data/CC-MAIN-2019-18/*
  - config_name: CC-MAIN-2019-13
    data_files:
      - split: train
        path: data/CC-MAIN-2019-13/*
  - config_name: CC-MAIN-2019-09
    data_files:
      - split: train
        path: data/CC-MAIN-2019-09/*
  - config_name: CC-MAIN-2019-04
    data_files:
      - split: train
        path: data/CC-MAIN-2019-04/*
  - config_name: CC-MAIN-2018-51
    data_files:
      - split: train
        path: data/CC-MAIN-2018-51/*
  - config_name: CC-MAIN-2018-47
    data_files:
      - split: train
        path: data/CC-MAIN-2018-47/*
  - config_name: CC-MAIN-2018-43
    data_files:
      - split: train
        path: data/CC-MAIN-2018-43/*
  - config_name: CC-MAIN-2018-39
    data_files:
      - split: train
        path: data/CC-MAIN-2018-39/*
  - config_name: CC-MAIN-2018-34
    data_files:
      - split: train
        path: data/CC-MAIN-2018-34/*
  - config_name: CC-MAIN-2018-30
    data_files:
      - split: train
        path: data/CC-MAIN-2018-30/*
  - config_name: CC-MAIN-2018-26
    data_files:
      - split: train
        path: data/CC-MAIN-2018-26/*
  - config_name: CC-MAIN-2018-22
    data_files:
      - split: train
        path: data/CC-MAIN-2018-22/*
  - config_name: CC-MAIN-2018-17
    data_files:
      - split: train
        path: data/CC-MAIN-2018-17/*
  - config_name: CC-MAIN-2018-13
    data_files:
      - split: train
        path: data/CC-MAIN-2018-13/*
  - config_name: CC-MAIN-2018-09
    data_files:
      - split: train
        path: data/CC-MAIN-2018-09/*
  - config_name: CC-MAIN-2018-05
    data_files:
      - split: train
        path: data/CC-MAIN-2018-05/*
  - config_name: CC-MAIN-2017-51
    data_files:
      - split: train
        path: data/CC-MAIN-2017-51/*
  - config_name: CC-MAIN-2017-47
    data_files:
      - split: train
        path: data/CC-MAIN-2017-47/*
  - config_name: CC-MAIN-2017-43
    data_files:
      - split: train
        path: data/CC-MAIN-2017-43/*
  - config_name: CC-MAIN-2017-39
    data_files:
      - split: train
        path: data/CC-MAIN-2017-39/*
  - config_name: CC-MAIN-2017-34
    data_files:
      - split: train
        path: data/CC-MAIN-2017-34/*
  - config_name: CC-MAIN-2017-30
    data_files:
      - split: train
        path: data/CC-MAIN-2017-30/*
  - config_name: CC-MAIN-2017-26
    data_files:
      - split: train
        path: data/CC-MAIN-2017-26/*
  - config_name: CC-MAIN-2017-22
    data_files:
      - split: train
        path: data/CC-MAIN-2017-22/*
  - config_name: CC-MAIN-2017-17
    data_files:
      - split: train
        path: data/CC-MAIN-2017-17/*
  - config_name: CC-MAIN-2017-13
    data_files:
      - split: train
        path: data/CC-MAIN-2017-13/*
  - config_name: CC-MAIN-2017-09
    data_files:
      - split: train
        path: data/CC-MAIN-2017-09/*
  - config_name: CC-MAIN-2017-04
    data_files:
      - split: train
        path: data/CC-MAIN-2017-04/*
  - config_name: CC-MAIN-2016-50
    data_files:
      - split: train
        path: data/CC-MAIN-2016-50/*
  - config_name: CC-MAIN-2016-44
    data_files:
      - split: train
        path: data/CC-MAIN-2016-44/*
  - config_name: CC-MAIN-2016-40
    data_files:
      - split: train
        path: data/CC-MAIN-2016-40/*
  - config_name: CC-MAIN-2016-36
    data_files:
      - split: train
        path: data/CC-MAIN-2016-36/*
  - config_name: CC-MAIN-2016-30
    data_files:
      - split: train
        path: data/CC-MAIN-2016-30/*
  - config_name: CC-MAIN-2016-26
    data_files:
      - split: train
        path: data/CC-MAIN-2016-26/*
  - config_name: CC-MAIN-2016-22
    data_files:
      - split: train
        path: data/CC-MAIN-2016-22/*
  - config_name: CC-MAIN-2016-18
    data_files:
      - split: train
        path: data/CC-MAIN-2016-18/*
  - config_name: CC-MAIN-2016-07
    data_files:
      - split: train
        path: data/CC-MAIN-2016-07/*
  - config_name: CC-MAIN-2015-48
    data_files:
      - split: train
        path: data/CC-MAIN-2015-48/*
  - config_name: CC-MAIN-2015-40
    data_files:
      - split: train
        path: data/CC-MAIN-2015-40/*
  - config_name: CC-MAIN-2015-35
    data_files:
      - split: train
        path: data/CC-MAIN-2015-35/*
  - config_name: CC-MAIN-2015-32
    data_files:
      - split: train
        path: data/CC-MAIN-2015-32/*
  - config_name: CC-MAIN-2015-27
    data_files:
      - split: train
        path: data/CC-MAIN-2015-27/*
  - config_name: CC-MAIN-2015-22
    data_files:
      - split: train
        path: data/CC-MAIN-2015-22/*
  - config_name: CC-MAIN-2015-18
    data_files:
      - split: train
        path: data/CC-MAIN-2015-18/*
  - config_name: CC-MAIN-2015-14
    data_files:
      - split: train
        path: data/CC-MAIN-2015-14/*
  - config_name: CC-MAIN-2015-11
    data_files:
      - split: train
        path: data/CC-MAIN-2015-11/*
  - config_name: CC-MAIN-2015-06
    data_files:
      - split: train
        path: data/CC-MAIN-2015-06/*
  - config_name: CC-MAIN-2014-52
    data_files:
      - split: train
        path: data/CC-MAIN-2014-52/*
  - config_name: CC-MAIN-2014-49
    data_files:
      - split: train
        path: data/CC-MAIN-2014-49/*
  - config_name: CC-MAIN-2014-42
    data_files:
      - split: train
        path: data/CC-MAIN-2014-42/*
  - config_name: CC-MAIN-2014-41
    data_files:
      - split: train
        path: data/CC-MAIN-2014-41/*
  - config_name: CC-MAIN-2014-35
    data_files:
      - split: train
        path: data/CC-MAIN-2014-35/*
  - config_name: CC-MAIN-2014-23
    data_files:
      - split: train
        path: data/CC-MAIN-2014-23/*
  - config_name: CC-MAIN-2014-15
    data_files:
      - split: train
        path: data/CC-MAIN-2014-15/*
  - config_name: CC-MAIN-2014-10
    data_files:
      - split: train
        path: data/CC-MAIN-2014-10/*
  - config_name: CC-MAIN-2013-48
    data_files:
      - split: train
        path: data/CC-MAIN-2013-48/*
  - config_name: CC-MAIN-2013-20
    data_files:
      - split: train
        path: data/CC-MAIN-2013-20/*
---

# πŸ“š FineWeb-Edu 
<center>
    <img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/wwRnEQydH9qdRtFofIE-A.png" alt="FineWeb-Edu: The finest collection of educational content the web has to offer">
</center>

> 1.3 trillion tokens of the finest educational data the 🌐 web has to offer

## What is it?

πŸ“š FineWeb-Edu  dataset consists of **1.3T tokens**  and  **5.4T tokens** ([FineWeb-Edu-score-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu-score-2)) of educational web pages filtered from 🍷 FineWeb dataset. This is the 1.3 trillion version.

To enhance FineWeb's quality, we developed an [educational quality classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier) using annotations generated by LLama3-70B-Instruct. We then used this classifier to retain only the most educational web pages. FineWeb-Edu outperforms FineWeb on popular benchmarks and shows the power of classifiers trained on synthetic data. 

The [Dataset Curation](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu#dataset-curation) section details the process for creating the dataset.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/QqXOM8h_ZjjhuCv71xmV7.png)

You can find a deduplicated version of FineWeb-edu in [SmolLM-Corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus). We find that the deduplication of this dataset doesn't have any impact on model performance in our ablation setup (1.8B trained on 350B tokens).

## What is being released?

Along with the dataset, which includes all filtered CommonCrawl dumps since 2013, we also release the educational classifier used for the filtering as well as the code for training it and running inference at: https://github.com/huggingface/cosmopedia/tree/main/classification 

## How to load the dataset
Similarily to FineWeb, You can load the full dataset or a specific crawl/dump. Dumps have the format `CC-MAIN-(year)-(week number)`.

### (Smaller) sample versions
Along with config `default` (all the data), and the configs for each individual dump, you can also download the following configs:
- `sample-350BT`: a subset randomly sampled from the whole dataset of around 350B gpt2 tokens 
- `sample-100BT`: a subset randomly sampled from the whole dataset of around 100B gpt2 tokens 
- `sample-10BT`: a subset randomly sampled from the whole dataset of around 10B gpt2 tokens 

`sample-10BT` was sampled from `sample-100BT` which in turn was sampled from `sample-350BT`.

### Using 🏭 [`datatrove`](https://github.com/huggingface/datatrove/)

```python
from datatrove.pipeline.readers import ParquetReader

# limit determines how many documents will be streamed (remove for all)
data_reader = ParquetReader("hf://datasets/HuggingFaceFW/fineweb-edu", glob_pattern="data/*/*.parquet", limit=1000)
# or to fetch a specific dump CC-MAIN-2024-10,  eplace "CC-MAIN-2024-10" with "sample/100BT" to use the 100BT sample
data_reader = ParquetReader("hf://datasets/HuggingFaceFW/fineweb-edu/CC-MAIN-2024-10", limit=1000) 
for document in data_reader():
    # do something with document
    print(document)

###############################    
# OR for a processing pipeline:
###############################

from datatrove.executor import LocalPipelineExecutor
from datatrove.pipeline.readers import ParquetReader
from datatrove.pipeline.filters import LambdaFilter
from datatrove.pipeline.writers import JsonlWriter

pipeline_exec = LocalPipelineExecutor(
    pipeline=[
        # replace "CC-MAIN-2024-10" with "sample/100BT" to use the 100BT sample
        ParquetReader("hf://datasets/HuggingFaceFW/fineweb-edu/CC-MAIN-2024-10", limit=1000),
        LambdaFilter(lambda doc: "hugging" in doc.text),
        JsonlWriter("some-output-path")
    ],
    tasks=10
)
pipeline_exec.run()
```

### Using `datasets`

```python
from datasets import load_dataset
# use name="sample-10BT" to use the 10BT sample
fw = load_dataset("HuggingFaceFW/fineweb-edu", name="CC-MAIN-2024-10", split="train", streaming=True)
```

## Dataset curation
A new approach has recently emerged for filtering LLM training datasets: using synthetic data to develop classifiers for identifying educational content. This technique was used in the trainings of [LLama3](https://ai.meta.com/blog/meta-llama-3-meta-ai-responsibility/) and [Phi3](https://arxiv.org/abs/2404.14219), but its large-scale impact on web data filtering hasn't been fully explored or published.

The highly popular Phi3 models were trained on 3.3 and 4.8 trillion tokens, with the paper stating: β€œOur training data consists of heavily filtered publicly available web data (according to the 'educational level') from various open internet sources, as well as synthetic LLM-generated data". Similarly, the LLama3 blog post notes: β€œWe found that previous generations of Llama are good at identifying high-quality data, so we used Llama 2 to help build the text-quality classifiers that are powering Llama 3.” However these classifiers and filtered datasets are not publicly available. To enhance FineWeb's quality, we developed an educational quality classifier using annotations generated by [LLama3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) to create FineWeb-Edu.

### Annotation
We used [Llama3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) to score 500k FineWeb samples for their educational quality on a scale from 0 to 5.

We explored various prompts and found that the additive scale by [Yuan et al.](https://arxiv.org/pdf/2401.10020) worked best. To avoid the LLM favoring highly technical pages like arXiv abstracts and submissions, we focused on grade-school and middle-school level knowledge. By setting a threshold of 3 (on a scale of 0 to 5) during the filtering process, we were able to also retain some high-level educational pages. The final prompt can be found [here](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier/blob/main/utils/prompt.txt).

We also experimented with different LLMs: Llama3-70B-Instruct, Mixtral-8x-7B-Instruct, and Mixtral-8x22B-Instruct. Llama 3 and Mixtral-8x22B produced similar scores, while Mixtral-8x7B tended to be more generous, not fully adhering to the score scale. Verga et al. suggest using multiple LLMs as juries. We tried averaging the scores from the three models, but this shifted the distribution to the right due to the higher scores from Mixtral-8x7B. Training on a dataset filtered with a classifier using jury annotations performed worse than using a classifier based on Llama3 annotations. We hypothesize that the jury-based approach retains more low-quality samples.

### Classifier training
We fine-tuned a Bert-like regression model using these annotations, based on [Snowflake-arctic-embed](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). When converted to a binary classification  using a score of 3 as a threshold for keeping and removing files, the model achieved an F1 score of 82%. The classification of FineWeb 15T tokens took 6k H100 GPU hours.

The classifier is available at: [HuggingFaceFW/fineweb-edu-classifier/](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier/)

### Filtering and results
**Note**: You can find more details about the ablations and results in the FineWeb [blog post](https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1).

We investigated the impact of using different thresholds for the filtering and found that threshold 3 gave the best overall results. Although using a threshold higher than 3 improves performance on knowledge and reasoning intensive benchmarks, it significantly degrades performance on HellaSwag and PIQA.

We then built πŸ“š FineWeb-Edu by filtering out samples with scores lower than 3. This removed 92% of the dataset, leaving us with 1.3T educational tokens. Our ablation demonstrated that this refined dataset surpasses 🍷 FineWeb and all other open web datasets, with remarkable improvements on educational benchmarks such as MMLU, ARC, and OpenBookQA. The plot below compares FineWeb-Edu to other web datasets:
 
![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/hJlyTgDzZpYuxO9LUm0PF.png)

To retain more tokens, we also experimented with a less strict threshold of 2 instead of 3. While being less performant than using threshold 3, it still outperformed FineWeb and it preserved 5.4T tokens. We release these two dataset as [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) and [FineWeb-Edu-score-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu-score-2) along with the [classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier).

You will find all the ablation models in [this collection](https://huggingface.co/collections/HuggingFaceFW/ablation-models-662457b0d213e8c14fe47f32). The FineWeb-Edu ablation model (trained on 350B tokens) is available at [https://huggingface.co/HuggingFaceFW/ablation-model-fineweb-edu](https://huggingface.co/HuggingFaceFW/ablation-model-fineweb-edu).

## Considerations for Using the Data
This section is copied from the parent dataset: [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb).

### Social Impact of Dataset

With the release of this dataset we aim to make model training more accessible to the machine learning community at large. 

While multiple open-weights models with strong performance have been publicly released in the past, more often than not these releases are not accompanied by the corresponding training dataset. This is unfortunate as the dataset specificities and characteristics have been demonstrated to have a very large impact and role in the performances of the models. As the creation of a high quality training dataset is a fundamental requirement to training an LLM capable of excelling at downstream tasks, with 🍷 FineWeb we (a) not only make the dataset creation process more transparent, by sharing our entire processing setup including the codebase used, we also (b) help alleviate the costs of dataset curation, both in time and in compute, for model creators by publicly releasing our dataset with the community.

### Discussion of Biases

Efforts were made to minimize the amount of NSFW and toxic content present in the dataset by employing filtering on the URL level. However, there are still a significant number of documents present in the final dataset that could be considered toxic or contain harmful content. As 🍷 FineWeb was sourced from the web as a whole, any harmful biases typically present in it may be reproduced on our dataset.

We deliberately avoided using machine learning filtering methods that define text quality based on the similarity to a β€œgold” source such as wikipedia or toxicity classifiers as these methods have been known to [disproportionately remove content in specific dialects](https://aclanthology.org/D16-1120/) and [overclassify as toxic text related to specific social identities](https://arxiv.org/pdf/2109.07445.pdf), respectively.

### Other Known Limitations

As a consequence of some of the filtering steps applied, it is likely that code content is not prevalent in our dataset. If you are training a model that should also perform code tasks, we recommend you use 🍷 FineWeb with a code dataset, such as [The Stack v2](https://huggingface.co/datasets/bigcode/the-stack-v2). You should also probably consider complementing 🍷 FineWeb with specialized curated sources (such as Wikipedia, for example) as they will likely have better formatting than the wikipedia content included in 🍷 FineWeb (we did not tailor the processing to individual websites).

## Additional Information

### Licensing Information

The dataset is released under the **Open Data Commons Attribution License (ODC-By) v1.0** [license](https://opendatacommons.org/licenses/by/1-0/). The use of this dataset is also subject to [CommonCrawl's Terms of Use](https://commoncrawl.org/terms-of-use).

### Future work

We plan to work on better educational classifier to improve the quality of FineWeb-Edu.

### Citation Information

```
@software{lozhkov2024fineweb-edu,
  author = {Lozhkov, Anton and Ben Allal, Loubna and von Werra, Leandro and Wolf, Thomas},
  title = {FineWeb-Edu},
  month = May,
  year = 2024,
  doi = { 10.57967/hf/2497 },
  url = {https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu}
}

```