Instructions to use pruhtopia/bert-toc-classification-95k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pruhtopia/bert-toc-classification-95k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pruhtopia/bert-toc-classification-95k")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pruhtopia/bert-toc-classification-95k") model = AutoModelForSequenceClassification.from_pretrained("pruhtopia/bert-toc-classification-95k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from pruhtopia/bert-toc-classification-95k: direct link, hf CLI and curl.
- Browser
- Download file 1.07 kB
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https://huggingface.co/pruhtopia/bert-toc-classification-95k/resolve/main/README.md
- Command line
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hf download hf://pruhtopia/bert-toc-classification-95k/README.md
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curl -L -o README.md https://huggingface.co/pruhtopia/bert-toc-classification-95k/resolve/main/README.md
1.07 kB
metadata
tags:
- autotrain
- text-classification
widget:
- text: I love AutoTrain
datasets:
- pruhtopia/multilingual-bert-toc-95k-dataset
license: apache-2.0
Model Trained Using AutoTrain
- Problem type: Text Classification
- Task: Legal Document Sequence Classification w/ bert-base-multilingual-cased
- id2label: [0: 'Caption', 1: 'Footnote', 2: 'Formula', 3: 'List-item', 4: 'Page-footer', 5: 'Page-header', 6: 'Picture', 7: 'Section-header', 8: 'Table', 9: 'Text', 10: 'Title']
- sample usage notebook here
Validation Metrics
loss: 0.5102838277816772
f1_macro: 0.605011586308457
f1_micro: 0.8910038281582305
f1_weighted: 0.8870714364293508
precision_macro: 0.6869883411452264
precision_micro: 0.8910038281582305
precision_weighted: 0.8858066104824025
recall_macro: 0.5550753643871188
recall_micro: 0.8910038281582305
recall_weighted: 0.8910038281582305
accuracy: 0.8910038281582305