Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Mofe/emotiscan_model_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mofe/emotiscan_model_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mofe/emotiscan_model_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mofe/emotiscan_model_2") model = AutoModelForSequenceClassification.from_pretrained("Mofe/emotiscan_model_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- befc1d134541a7ef0d2a0c0141095ebdb302d292be10098af92f56c7a7f3328e
- Size of remote file:
- 1.34 GB
- SHA256:
- ae666e1da7093df88f9927c9e94578b9da8f7a85d169176b1e08bda375c3865b
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