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
File size: 1,068 Bytes
116932b 4d62ab2 116932b 4d62ab2 e807e4c 116932b fbc93bc 18588d2 e807e4c 116932b 4d62ab2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ---
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](https://huggingface.co/google-bert/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](https://colab.research.google.com/drive/1tSpV0RC12LDNFbWEq6kdiUCIJ8DkHHU_?usp=sharing)
## 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 |