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:
- 9456ca3c176ccacbc2e888ca86e16c1df0572b7327660c382cac0785d9afb28f
- Size of remote file:
- 4.41 kB
- SHA256:
- a60e4287d56b88f39c2127335bf246786ebc7c7315c199be535b3a3ad98011de
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