Text Classification
Transformers
PyTorch
English
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use MachineLearningLawyer/conservative-101-rejection-examiner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MachineLearningLawyer/conservative-101-rejection-examiner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MachineLearningLawyer/conservative-101-rejection-examiner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MachineLearningLawyer/conservative-101-rejection-examiner") model = AutoModelForSequenceClassification.from_pretrained("MachineLearningLawyer/conservative-101-rejection-examiner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 131 Bytes
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