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 pytorch_model.bin from MachineLearningLawyer/conservative-101-rejection-examiner: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/MachineLearningLawyer/conservative-101-rejection-examiner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MachineLearningLawyer/conservative-101-rejection-examiner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MachineLearningLawyer/conservative-101-rejection-examiner/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- d99d3b37e12993bfb5eff54bbfeef1cb1ff2b1addf218acf50dfd2e440e5104e
- Size of remote file:
- 438 MB
- SHA256:
- cc0fc7c4aef6b85ffb77bbcbf385616cd8e89ebd4394f35abf32740aea094738
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