metadata

language:
  - 'no'
  - nb
  - nn
license: cc-by-4.0
datasets:
  - ltg/norec_sentence
pipeline_tag: text-classification

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

Category	F1
Negative_F1	0.670241
Positive_F1	0.832918
Neutral_F1	0.850082
Mixed_F1	0.580645
Weighted_avg_F1	0.799663