Text Classification
Transformers
TensorBoard
Safetensors
Maltese
bert
Eval Results (legacy)
text-embeddings-inference
Instructions to use MLRS/BERTu_sentiment-mlt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLRS/BERTu_sentiment-mlt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MLRS/BERTu_sentiment-mlt")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MLRS/BERTu_sentiment-mlt") model = AutoModelForSequenceClassification.from_pretrained("MLRS/BERTu_sentiment-mlt") - Notebooks
- Google Colab
- Kaggle
Sentiment Analysis Categories - Query
#1
by julnnev - opened
Would it be possible to let me know what labels 0 and 1 correspond to and whether this model can also classify text as 'neutral'? Thank you.
Hello, thank you for your interest!
0 & 1 map to negative & positive, respectively. These labels come from the data.
This model cannot predict the neutral class, but a new model (BERTu_SentiMalti) has just been released that can predict the neutral class in addition to positive & negative. This new model has been trained on an expanded version of the data used for this model.
Thank you!
julnnev changed discussion status to closed
julnnev changed discussion status to open
KurtMica changed discussion status to closed