README.md

---
pipeline_tag: zero-shot-classification
language:
- da
- no
- nb
- sv
license: mit
datasets:
- strombergnlp/danfever
- KBLab/overlim
- MoritzLaurer/multilingual-NLI-26lang-2mil7
model-index:
- name: nb-bert-base-nli-scandi
  results: []
widget:
- example_title: Danish
  text: Mexicansk bokser advarer Messi - 'Du skal bede til gud, om at jeg ikke finder dig'
  candidate_labels: sundhed, politik, sport, religion
- example_title: Norwegian
  text: Regjeringen i Russland hevder Norge fører en politikk som vil føre til opptrapping i Arktis og «den endelige ødeleggelsen av russisk-norske relasjoner».
  candidate_labels: helse, politikk, sport, religion
- example_title: Swedish
  text: Så luras kroppens immunförsvar att bota cancer
  candidate_labels: hälsa, politik, sport, religion
inference:
  parameters:
    hypothesis_template: "Dette eksempel handler om {}"
---

# ScandiNLI - Natural Language Inference model for Scandinavian Languages

This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) for Natural Language Inference in Danish, Norwegian Bokmål and Swedish.

It has been fine-tuned on a dataset composed of [DanFEVER](https://aclanthology.org/2021.nodalida-main.pdf#page=439) as well as machine translated versions of [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) and [CommitmentBank](https://doi.org/10.18148/sub/2019.v23i2.601) into all three languages, and machine translated versions of [FEVER](https://aclanthology.org/N18-1074/) and [Adversarial NLI](https://aclanthology.org/2020.acl-main.441/) into Swedish.

The three languages are sampled equally during training, and they're validated on validation splits of [DanFEVER](https://aclanthology.org/2021.nodalida-main.pdf#page=439) and machine translated versions of [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) for Swedish and Norwegian Bokmål, sampled equally.


## Quick start

You can use this model in your scripts as follows:

```python
>>> from transformers import pipeline
>>> classifier = pipeline(
...     "zero-shot-classification",
...     model="alexandrainst/nb-bert-base-nli-scandi",
... )
>>> classifier(
...     "Mexicansk bokser advarer Messi - 'Du skal bede til gud, om at jeg ikke finder dig'",
...     candidate_labels=['sundhed', 'politik', 'sport', 'religion'],
...     hypothesis_template="Dette eksempel handler om {}",
... )
{'sequence': "Mexicansk bokser advarer Messi - 'Du skal bede til gud, om at jeg ikke finder dig'",
 'labels': ['sport', 'religion', 'sundhed', 'politik'],
 'scores': [0.724335789680481,
  0.1176532730460167,
  0.08848614990711212,
  0.06952482461929321]}
```


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 4242
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- max_steps: 50,000