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:
- b0d38e3dcac05b1e7e28473a4e1c44219f77d11e02230ea44b5e9a4381e89814
- Size of remote file:
- 438 MB
- SHA256:
- f7b6965450666cbb56f8dbdbcc1a71c380360b667fc7ba0a6f6ea53371f8cbfe
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