# anli by AI at Meta: AI Training Dataset
Source: https://savrn.com/datasets/anli
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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## Dataset Card

The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset, The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure. ANLI is much more difficult than its predecessors including SNLI and MNLI. It contains three rounds. Each round has train/dev/test splits. English An example of 'trainr2' looks as follows. The data fields are the same among all splits. - uid: a string feature. - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). - reason: a string feature. Thanks to @thomwolf, @easonnie, @lhoestq…

Excerpt from the card by AI at Meta, licensed cc-by-nc-4.0.

## Structure

### plain_text 169,265 rows

| Split | Rows | Size |
| --- | --- | --- |
| train_r1 | 16,946 | 7.6 MB |
| dev_r1 | 1,000 | 573.6 KB |
| test_r1 | 1,000 | 575.0 KB |
| train_r2 | 45,460 | 20.0 MB |
| dev_r2 | 1,000 | 556.2 KB |
| test_r2 | 1,000 | 572.8 KB |
| train_r3 | 100,459 | 67.1 MB |
| dev_r3 | 1,200 | 673.1 KB |
| test_r3 | 1,200 | 666.9 KB |

uidstringpremisestringhypothesisstringlabelClassLabelreasonstring

## Details

Repository

facebook/anli

Publisher

AI at Meta

Task category

Text classification

Tags

Not stated by the source

Size category

100K<n<1M

Languages

en

Revision

8e4813d81f46d313dac7892e1c28076917cfcdf9

Last updated

2023-12-21

## SAVRN's Notes on anli

ANLI is adversarial natural language inference: 169,265 rows, 26.3 MB, 11 files, built over three rounds with each round carrying its own train, dev and test splits, where dev and test sit at 1,000 rows per round. The inbound list on our hub shows the actual training use: a wall of NLI encoders, including the DeBERTa-v3 mnli-fever-anli family and ModernBERT-base and ModernBERT-large nli variants.

The license is CC-BY-NC-4.0, which bars commercial use: fine for research fine-tuning and evaluation, off-limits for a product training run. At 181.1k downloads a month, it remains the standard hard NLI benchmark, and its adversarial human-and-model-in-the-loop construction is exactly why it still separates models. Storage is a non-issue at 26.3 MB in English; the NC clause is the decision point.

SAVRN Research

## Files

11 files, 26.3 MB in total.

Data9 files · 26.3 MB

Documentation1 file · 8.0 KB

Repository1 file · 1.2 KB

Every file

| File | Type | Size | SHA-256 |
| --- | --- | --- | --- |
| plain_text/dev_r1-00000-of-00001.parquet | Data | 351.5 KB | 72e27463177b |
| plain_text/dev_r2-00000-of-00001.parquet | Data | 350.6 KB | 43e4673665de |
| plain_text/dev_r3-00000-of-00001.parquet | Data | 434.0 KB | 61775ec09351 |
| plain_text/test_r1-00000-of-00001.parquet | Data | 353.4 KB | c4a3d304c467 |
| plain_text/test_r2-00000-of-00001.parquet | Data | 361.5 KB | df5daccdd562 |
| plain_text/test_r3-00000-of-00001.parquet | Data | 434.6 KB | 3232c4217979 |
| plain_text/train_r1-00000-of-00001.parquet | Data | 3.1 MB | de2d038ae67f |
| plain_text/train_r2-00000-of-00001.parquet | Data | 6.5 MB | 209f4a15bf77 |
| plain_text/train_r3-00000-of-00001.parquet | Data | 14.3 MB | c1d3f614d673 |
| README.md | Documentation | 8.0 KB | — |
| .gitattributes | Repository | 1.2 KB | — |

## License and Download

License

cc-by-nc-4.0

Access

No access gate

[Download from AI at Meta](https://huggingface.co/datasets/facebook/anli)

Released by AI at Meta through its official repository on Hugging Face. [Read the license](https://creativecommons.org/licenses/by-nc/4.0/).

## Models Trained on This Dataset

- Trained on (disclosed)[mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://savrn.com/models/mdeberta-v3-base-xnli-multilingual-nli-2mil7)
- Trained on (disclosed)[DeBERTa-v3-base-mnli-fever-anli](https://savrn.com/models/deberta-v3-base-mnli-fever-anli)
- Trained on (disclosed)[DeBERTa-v3-large-mnli-fever-anli-ling-wanli](https://savrn.com/models/deberta-v3-large-mnli-fever-anli-ling-wanli)
- Trained on (disclosed)[ModernBERT-base-nli](https://savrn.com/models/modernbert-base-nli)
- Trained on (disclosed)[ModernBERT-large-nli](https://savrn.com/models/modernbert-large-nli)
- Trained on (disclosed)[DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary](https://savrn.com/models/deberta-v3-xsmall-mnli-fever-anli-ling-binary)
- Trained on (disclosed)[deberta-small-long-nli](https://savrn.com/models/deberta-small-long-nli)
- Trained on (disclosed)[deberta-base-long-nli](https://savrn.com/models/deberta-base-long-nli)

## AI at Meta

[All models and datasets](https://savrn.com/model-publishers/facebook)

## Explore More

- [The dataset directory](https://savrn.com/datasets)
- [The model directory](https://savrn.com/models)
- [Research papers](https://savrn.com/papers)

## Source

- Repository metadata, read 2026-10-19.
- [AI at Meta website](https://ai.meta.com/resources/models-and-libraries/)
- [Hugging Face record](https://huggingface.co/datasets/facebook/anli)
- [How the hub is built](https://savrn.com/model-hub/methodology)