Text Classification
Transformers
PyTorch
TensorFlow
ONNX
Safetensors
hn
Bengali
Mongolian
xlm-roberta
Text Classification
text-embeddings-inference
Instructions to use seanbenhur/MuLTiGENBiaS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seanbenhur/MuLTiGENBiaS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="seanbenhur/MuLTiGENBiaS")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("seanbenhur/MuLTiGENBiaS") model = AutoModelForSequenceClassification.from_pretrained("seanbenhur/MuLTiGENBiaS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93b418e012a73baa6f6b0613f3a34f759629dead60631099e22ed276b8343406
- Size of remote file:
- 1.11 GB
- SHA256:
- d4538de19f472542e388f30b399fa4015b6c197f113ac702a786bc42d86a40db
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