Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use James520/Grammar_Check with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use James520/Grammar_Check with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="James520/Grammar_Check")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("James520/Grammar_Check") model = AutoModelForSequenceClassification.from_pretrained("James520/Grammar_Check", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-1069/optimizer.pt from James520/Grammar_Check: direct link, hf CLI and curl.
- Browser
- Download file 876 MB
-
https://huggingface.co/James520/Grammar_Check/resolve/main/checkpoint-1069/optimizer.pt
- Command line
-
hf download hf://James520/Grammar_Check/checkpoint-1069/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/James520/Grammar_Check/resolve/main/checkpoint-1069/optimizer.pt
876 MB
- Xet hash:
- 8d4e527dd5f7fb4652a72992899bba9eabae36bb69fa5d9f99203d55bc4c9def
- Size of remote file:
- 876 MB
- SHA256:
- ef6f7ee792bec93fa3c08020a82ea0b0f096e955740c1b62f0eb2e33a09e5b73
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