Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use katzenbach/testMetaphor4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use katzenbach/testMetaphor4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="katzenbach/testMetaphor4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("katzenbach/testMetaphor4") model = AutoModelForSequenceClassification.from_pretrained("katzenbach/testMetaphor4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45abe1e54f63102ea548b32f57a0c1ce651d7a454c056bb74874c3911299adbc
- Size of remote file:
- 4.6 kB
- SHA256:
- eacfe2507d5e87b17cc956a11a487f047b9264ae6c830c8115ff185f9ca52dbc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.