Sentence-level Sentiment Analysis model for Norwegian text

This model is a fine-tuned version of ltg/norbert3-base for text classification.

Training data

The dataset used for fine-tuning is ltg/norec_sentence, the mixed subset with four sentement categories:

[0]: Negative, 
[1]: Positive, 
[2]: Neutral
[0,1]: Mixed

Quick start

You can use this model for inference as follows:

>>> from transformers import pipeline
>>> origin = "ltg/norbert3-large_sentence-sentiment"
>>> pipe = transformers.pipeline( "text-classification",
... model = origin,
... trust_remote_code=origin.startswith("ltg/norbert3"),
... config= origin,
... tokenizer = AutoTokenizer.from_pretrained(origin)
... )
>>> preds = pipe(["Hans hese, litt såre stemme kler bluesen, men denne platen kommer neppe til å bli blant hans største kommersielle suksesser.",
... "Borten-regjeringen gjorde ikke jobben sin." ])
>>> for p in preds:
... print(p)

Output:

The model 'NorbertForSequenceClassification' is not supported for text-classification. Supported models are ['AlbertForSequenceClassification', ...
{'label': 'Mixed', 'score': 0.7435498237609863}
{'label': 'Negative', 'score': 0.765734851360321}

Training hyperparameters

• per_device_train_batch_size: 32
• learning_rate: 1e-05
• gradient_accumulation_steps: 1
• num_train_epochs: 10 (best epoch 2)

Evaluation

Datasets and models fine-tuned for sentiment analysis • 6 items • Updated May 13