Text Classification
Transformers
PyTorch
English
roberta
emotion-classification
multi-label-classification
goemotions
sentiment-analysis
focal-loss
threshold-optimization
Instructions to use Lakssssshya/roberta-large-goemotions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lakssssshya/roberta-large-goemotions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Lakssssshya/roberta-large-goemotions")# Load model directly from transformers import AutoTokenizer, RobertaForMultiLabelClassification tokenizer = AutoTokenizer.from_pretrained("Lakssssshya/roberta-large-goemotions") model = RobertaForMultiLabelClassification.from_pretrained("Lakssssshya/roberta-large-goemotions", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f5fdea12af9a3b9cc1fd254b46b8998ef6b5397b0f42d28a9dce0a245abb4c7e
- Size of remote file:
- 1.42 GB
- SHA256:
- bea1e6bc8806c85beb9862be38b0dc49f06e768c0ed92c9d7575a8a8d9afffb6
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