Scandi Nli Base

🚀 ScandiNLI - Natural Language Inference model for Scandinavian Languages

ScandiNLI is a fine - tuned model for Natural Language Inference in Danish, Norwegian Bokmål, and Swedish, offering different sizes to meet various needs.

📋 Model Information

🚀 Quick Start

You can use this model in your scripts as follows:

Basic Usage

>>> from transformers import pipeline
>>> classifier = pipeline(
...     "zero-shot-classification",
...     model="alexandrainst/scandi-nli-base",
... )
>>> 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]}

✨ Features

We have released three models for Scandinavian NLI, of different sizes:

• [alexandrainst/scandi - nli - large - v2](https://huggingface.co/alexandrainst/scandi - nli - large - v2)
• [alexandrainst/scandi - nli - large](https://huggingface.co/alexandrainst/scandi - nli - large)
• alexandrainst/scandi - nli - base (this)
• [alexandrainst/scandi - nli - small](https://huggingface.co/alexandrainst/scandi - nli - small)

A demo of the large - v2 model can be found in [this Hugging Face Space](https://huggingface.co/spaces/alexandrainst/zero - shot - classification) - check it out!

📊 Performance

We evaluate the models in Danish, Swedish, and Norwegian Bokmål separately. In all cases, we report Matthew's Correlation Coefficient (MCC), macro - average F1 - score, as well as accuracy.

Scandinavian Evaluation

The Scandinavian scores are the average of the Danish, Swedish, and Norwegian scores.

Danish Evaluation

We use a test split of the [DanFEVER dataset](https://aclanthology.org/2021.nodalida - main.pdf#page = 439) to evaluate the Danish performance of the models. The test split is generated using this gist.

Swedish Evaluation

We use the test split of the machine - translated version of the MultiNLI dataset to evaluate the Swedish performance of the models. We acknowledge that not evaluating on a gold - standard dataset is not ideal, but unfortunately we are not aware of any NLI datasets in Swedish.

Norwegian Evaluation

We use the test split of the machine - translated version of the MultiNLI dataset to evaluate the Norwegian performance of the models. We acknowledge that not evaluating on a gold - standard dataset is not ideal, but unfortunately we are not aware of any NLI datasets in Norwegian.

🔧 Technical Details

Training procedure

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 and CommitmentBank 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 training split of DanFEVER is generated using this gist.

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 for Swedish and Norwegian Bokmål, sampled equally.

Check out the Github repository for the code used to train the ScandiNLI models, and the full training logs can be found in this Weights and Biases report.

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

📄 License

This model is licensed under the Apache 2.0 license.