Model Card for LayoutLM for Document Classification
Model Details
Model Description
This is a fine-tuned version of the multi-modal LayoutLM model for the task of classification on documents.
- Developed by: Impira team
- Shared by [Optional]: Hugging Face
- Model type: Text Classification
- Language(s) (NLP): en
- License: cc-by-nc-sa-4.0
- Related Models: layoutlm
- Parent Model: More information needed
- Resources for more information:
Uses
Direct Use
Text Classification
Downstream Use [Optional]
More information needed
Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Training Details
Training Data
More information needed
Training Procedure
More information needed
Preprocessing
More information needed
Speeds, Sizes, Times
Num_attention_head: 12 Num_hidden_layer:12, Vocab_size: 30522
Evaluation
Testing Data, Factors & Metrics
Testing Data
More information needed
Factors
More information needed
Metrics
More information needed
Results
More information needed
Model Examination
More information needed
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: More information needed
- Hours used: More information needed
- Cloud Provider: More information needed
- Compute Region: More information needed
- Carbon Emitted: More information needed
Technical Specifications [optional]
Model Architecture and Objective
More information needed
Compute Infrastructure
More information needed
Hardware
More information needed
Software
Transformers version: 4.4.0.dev0
Citation
BibTeX:
More information needed}
APA:
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Glossary [optional]
More information needed
More Information [optional]
More information needed
Model Card Authors [optional]
Impira team in collaboration with Ezi Ozoani and the Hugging Face team.
Model Card Contact
More information needed
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-classifier")
model = AutoModelForSequenceClassification.from_pretrained("impira/layoutlm-document-classifier")