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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---
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
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