Text Classification
Transformers
PyTorch
Safetensors
English
roberta
emotions
multi-class-classification
multi-label-classification
text-embeddings-inference
Instructions to use Linsad/text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Linsad/text_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Linsad/text_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Linsad/text_classification") model = AutoModelForSequenceClassification.from_pretrained("Linsad/text_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e443a993e6371cc8ab720171858f131ca53c36a5368406718144716e1d0bd04d
- Size of remote file:
- 499 MB
- SHA256:
- 4fd088956d38ce7ca956815b0203caf6f29b492b04c22c50d67542b3e02c449d
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