Create README.md

README.md

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+---
+pipeline_tag: zero-shot-classification
+language:
+- da
+- no
+- nb
+- sv
+license: mit
+datasets:
+- strombergnlp/danfever
+- mnli_da
+- mnli_sv
+- mnli_nb
+- cb_da
+- cb_sv
+- cb_nb
+- fever_sv
+- anli_sv
+model-index:
+- name: nb-bert-large-ner-scandi
+  results: []
+widget:
+- example_title: Nyhetsartikkel om FHI
+  text: Folkehelseinstituttets mest optimistiske anslag er at alle voksne er ferdigvaksinert innen midten av september.
+  candidate_labels: helse, politikk, sport, religion
+---
+
+# ScandiNLI - Natural Language Inference model for Scandinavian Languages
+
+This model is a fine-tuned version of [NbAiLab/nb-bert-large](https://huggingface.co/NbAiLab/nb-bert-large) 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-large-nli-scandi")
+>>> classifier(
+...     'Folkehelseinstituttets mest optimistiske anslag er at alle over 18 år er ferdigvaksinert innen midten av september.',
+...     candidate_labels=['helse', 'politikk', 'sport', 'religion'],
+...     hypothesis_template="Dette eksempelet er {}",
+)
+{
+    'labels': ['helse', 'politikk', 'sport', 'religion'], 
+    'scores': [0.4210019111633301, 0.0674605593085289, 0.000840459018945694, 0.0007541406666859984],
+    'sequence': 'Folkehelseinstituttets mest optimistiske anslag er at alle over 18 år er ferdigvaksinert innen midten av september.',
+}
+```
+
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 4242
+- gradient_accumulation_steps: 16
+- 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