Text Classification
Transformers
TensorFlow
Keras
roberta
multi-label-classification
emotion-detection
tensorflow
generated_from_keras_callback
Eval Results (legacy)
text-embeddings-inference
Instructions to use athallabf/fp-ai-modul-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use athallabf/fp-ai-modul-6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="athallabf/fp-ai-modul-6")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("athallabf/fp-ai-modul-6") model = AutoModelForSequenceClassification.from_pretrained("athallabf/fp-ai-modul-6") - Keras
How to use athallabf/fp-ai-modul-6 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://athallabf/fp-ai-modul-6") - Notebooks
- Google Colab
- Kaggle
Ctrl+K