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
Download tokenizer.json from MachineLearningLawyer/conservative-101-rejection-examiner: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/MachineLearningLawyer/conservative-101-rejection-examiner/resolve/main/tokenizer.json
- Command line
-
hf download hf://MachineLearningLawyer/conservative-101-rejection-examiner/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/MachineLearningLawyer/conservative-101-rejection-examiner/resolve/main/tokenizer.json
712 kB
- Xet hash:
- 2601b815a02ea742b91cf0cdcf3de298b909704a79f2c121c9073d758767bc67
- Size of remote file:
- 712 kB
- SHA256:
- f83a7f24eee69f73e1a5cad5a09dac6e3c6731c461540cb3498b22285d3ca6ed
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