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
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Download README.md from James520/Grammar_Check: direct link, hf CLI and curl.
- Browser
- Download file 408 Bytes
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https://huggingface.co/James520/Grammar_Check/resolve/main/README.md
- Command line
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hf download hf://James520/Grammar_Check/README.md
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curl -L -o README.md https://huggingface.co/James520/Grammar_Check/resolve/main/README.md
408 Bytes
| tags: | |
| - autotrain | |
| - text-classification | |
| base_model: google-bert/bert-base-uncased | |
| widget: | |
| - text: "I love AutoTrain" | |
| # Model Trained Using AutoTrain | |
| - Problem type: Text Classification | |
| ## Validation Metrics | |
| loss: 0.4740733206272125 | |
| f1: 0.8560558021559924 | |
| precision: 0.7885514018691588 | |
| recall: 0.9361997226074896 | |
| auc: 0.8029694782091815 | |
| accuracy: 0.7823585810162992 | |