Text Classification
Transformers
PyTorch
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence") model = AutoModelForSequenceClassification.from_pretrained("DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9661810dc409c05c50c887f57b5654c5ed6ea2bcc94676bb3ecf418c051d735
- Size of remote file:
- 2.24 GB
- SHA256:
- fab7abe9f6785b5e3f6b4edd65a4b6b5ca123723a497c45a28bf70be4c01c218
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