---
library_name: transformers
base_model:
- answerdotai/ModernBERT-base
license: apache-2.0
language:
- en
pipeline_tag: zero-shot-classification
datasets:
- nyu-mll/glue
- facebook/anli
tags:
- instruct
- natural-language-inference
- nli
---

# Model Card for Model ID

ModernBERT multi-task fine-tuned on tasksource NLI tasks, including MNLI, ANLI, SICK, WANLI, doc-nli, LingNLI, FOLIO, FOL-NLI, LogicNLI, Label-NLI and all datasets in the below table).
This is the equivalent of an "instruct" version.

Test accuracy at 100k training steps. 250k steps version coming around 25 december.

| test_name                            |   test_accuracy |
|:-------------------------------------|----------------:|
| glue/mnli                            |            0.91 |
| glue/qnli                            |            0.93 |
| glue/rte                             |            0.86 |
| super_glue/cb                        |            0.89 |
| anli/a1                              |            0.62 |
| anli/a2                              |            0.47 |
| anli/a3                              |            0.42 |
| sick/label                           |            0.92 |
| sick/entailment_AB                   |            0.84 |
| snli                                 |            0.91 |
| scitail/snli_format                  |            0.95 |
| hans                                 |            1    |
| WANLI                                |            0.71 |
| recast/recast_sentiment              |            0.98 |
| recast/recast_verbcorner             |            0.94 |
| recast/recast_ner                    |            0.87 |
| recast/recast_factuality             |            0.93 |
| recast/recast_puns                   |            0.93 |
| recast/recast_kg_relations           |            0.94 |
| recast/recast_verbnet                |            0.88 |
| recast/recast_megaveridicality       |            0.87 |
| probability_words_nli/usnli          |            0.77 |
| probability_words_nli/reasoning_1hop |            0.99 |
| probability_words_nli/reasoning_2hop |            0.9  |
| nan-nli                              |            0.85 |
| nli_fever                            |            0.72 |
| breaking_nli                         |            1    |
| conj_nli                             |            0.71 |
| fracas                               |            0.86 |
| dialogue_nli                         |            0.88 |
| mpe                                  |            0.73 |
| dnc                                  |            0.9  |
| recast_white/fnplus                  |            0.81 |
| recast_white/sprl                    |            0.92 |
| recast_white/dpr                     |            0.61 |
| robust_nli/IS_CS                     |            0.76 |
| robust_nli/LI_LI                     |            0.98 |
| robust_nli/ST_WO                     |            0.85 |
| robust_nli/PI_SP                     |            0.74 |
| robust_nli/PI_CD                     |            0.8  |
| robust_nli/ST_SE                     |            0.78 |
| robust_nli/ST_NE                     |            0.86 |
| robust_nli/ST_LM                     |            0.81 |
| robust_nli_is_sd                     |            1    |
| robust_nli_li_ts                     |            0.91 |
| add_one_rte                          |            0.91 |
| cycic_classification                 |            0.83 |
| lingnli                              |            0.82 |
| monotonicity-entailment              |            0.95 |
| scinli                               |            0.79 |
| naturallogic                         |            0.91 |
| syntactic-augmentation-nli           |            0.95 |
| autotnli                             |            0.92 |
| defeasible-nli/atomic                |            0.76 |
| defeasible-nli/snli                  |            0.79 |
| help-nli                             |            0.91 |
| nli-veridicality-transitivity        |            0.99 |
| lonli                                |            0.99 |
| dadc-limit-nli                       |            0.67 |
| folio                                |            0.59 |
| tomi-nli                             |            0.53 |
| temporal-nli                         |            0.92 |
| counterfactually-augmented-snli      |            0.74 |
| cnli                                 |            0.81 |
| logiqa-2.0-nli                       |            0.57 |
| mindgames                            |            0.94 |
| ConTRoL-nli                          |            0.65 |
| logical-fallacy                      |            0.31 |
| conceptrules_v2                      |            0.99 |
| zero-shot-label-nli                  |            0.74 |
| scone                                |            0.97 |
| monli                                |            0.98 |
| SpaceNLI                             |            1    |
| propsegment/nli                      |            0.91 |
| SDOH-NLI                             |            1    |
| scifact_entailment                   |            0.78 |
| AdjectiveScaleProbe-nli              |            0.99 |
| resnli                               |            0.99 |
| semantic_fragments_nli               |            0.99 |
| dataset_train_nli                    |            0.88 |
| ruletaker                            |            0.91 |
| PARARULE-Plus                        |            1    |
| logical-entailment                   |            0.73 |
| nope                                 |            0.54 |
| LogicNLI                             |            0.65 |
| contract-nli/contractnli_a/seg       |            0.87 |
| contract-nli/contractnli_b/full      |            0.78 |
| nli4ct_semeval2024                   |            0.6  |
| biosift-nli                          |            0.88 |
| SIGA-nli                             |            0.54 |
| FOL-nli                              |            0.71 |
| doc-nli                              |            0.82 |
| mctest-nli                           |            0.89 |
| idioms-nli                           |            0.86 |
| lifecycle-entailment                 |            0.71 |
| MSciNLI                              |            0.82 |
| hover-3way/nli                       |            0.9  |
| seahorse_summarization_evaluation    |            0.82 |
| babi_nli                             |            0.94 |
| gen_debiased_nli                     |            0.9  |

# Usage

## [ZS] Zero-shot classification pipeline
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",model="tasksource/ModernBERT-base-nli")

text = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(text, candidate_labels)
```
NLI training data of this model includes [label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli), a NLI dataset specially constructed to improve this kind of zero-shot classification.

## [NLI] Natural language inference pipeline

```python
from transformers import pipeline
pipe = pipeline("text-classification",model="tasksource/ModernBERT-base-nli")
pipe([dict(text='there is a cat',
  text_pair='there is a black cat')]) #list of (premise,hypothesis)
```

## Backbone for further fune-tuning

This checkpoint has stronger reasoning and fine-grained abilities than the base version and can be used for further fine-tuning.

# Citation

```
@inproceedings{sileo-2024-tasksource,
    title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
    author = "Sileo, Damien",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1361",
    pages = "15655--15684",
}
```