Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use tgamstaetter/mult_tf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tgamstaetter/mult_tf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tgamstaetter/mult_tf")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tgamstaetter/mult_tf") model = AutoModelForSequenceClassification.from_pretrained("tgamstaetter/mult_tf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a8584ed087fc14c6e3ede22624085937eb76ca48695ff9181a822f7b715f807
- Size of remote file:
- 3.96 kB
- SHA256:
- 46c6fe8328e8d41471af42c8d6ff83ba2a2edbc9260cc5e136e67e07182c3d89
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