Token Classification
Transformers
TensorBoard
Safetensors
English
lilt
document understanding
document layout detection
nlp
doclaynet
RoBERTa
Instructions to use MuafiraThasni/layout-classification-doclaynet-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MuafiraThasni/layout-classification-doclaynet-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MuafiraThasni/layout-classification-doclaynet-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("MuafiraThasni/layout-classification-doclaynet-base") model = AutoModelForTokenClassification.from_pretrained("MuafiraThasni/layout-classification-doclaynet-base") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
datasets:
- MuafiraThasni/DocLayNet-base_paragraphs_encoded_ml512
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
tags:
- document understanding
- document layout detection
- nlp
- doclaynet
- RoBERTa
This is a document layout classification model. This model is a fine-tuned version of nielsr/lilt-xlm-roberta-base with the DocLayNet base dataset.
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- Developed by: Muafira Thasni
- Language(s) (NLP): English
- License: MIT
- Finetuned from model: nielsr/lilt-xlm-roberta-base
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