ltg/norbert3-base_sentence-sentiment
351
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-base_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': 0.9230353236198425}
{'label': 'Negative', 'score': 0.7348112463951111}Training hyperparameters
- perdevicetrainbatchsize: 16
- learning_rate: 1e-05
- gradientaccumulationsteps: 1
- numtrainepochs: 10 (best epoch 5)