Instructions to use pruhtopia/bert-toc-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pruhtopia/bert-toc-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pruhtopia/bert-toc-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pruhtopia/bert-toc-classification") model = AutoModelForSequenceClassification.from_pretrained("pruhtopia/bert-toc-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from pruhtopia/bert-toc-classification: direct link, hf CLI and curl.
- Browser
- Download file 593 Bytes
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https://huggingface.co/pruhtopia/bert-toc-classification/resolve/main/README.md
- Command line
-
hf download hf://pruhtopia/bert-toc-classification/README.md
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curl -L -o README.md https://huggingface.co/pruhtopia/bert-toc-classification/resolve/main/README.md
593 Bytes
metadata
tags:
- autotrain
- text-classification
widget:
- text: I love AutoTrain
datasets:
- autotrain-rqsu1-nelrs/autotrain-data
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.8727797865867615
f1_macro: 0.4743843561639563
f1_micro: 0.7148333333333333
f1_weighted: 0.6973841930196186
precision_macro: 0.689196184834718
precision_micro: 0.7148333333333333
precision_weighted: 0.7078896728033317
recall_macro: 0.41641019062275997
recall_micro: 0.7148333333333333
recall_weighted: 0.7148333333333333
accuracy: 0.7148333333333333