Create README.md

README.md

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+---
+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-ner-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', 'politik', 'sundhed'],
+ 'scores': [0.6134647727012634,
+  0.30309760570526123,
+  0.05021871626377106,
+  0.03321893885731697]}
+```
+
+
+## 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