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--- |
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language: en |
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license: cc-by-nc-sa-4.0 |
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tags: |
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- layoutlm |
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- document-classification |
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- pdf |
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- invoices |
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--- |
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# Model Card for LayoutLM for Document Classification |
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# Model Details |
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## Model Description |
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This is a fine-tuned version of the multi-modal LayoutLM model for the task of classification on documents. |
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- **Developed by:** Impira team |
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- **Shared by [Optional]:** Hugging Face |
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- **Model type:** Text Classification |
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- **Language(s) (NLP):** en |
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- **License:** cc-by-nc-sa-4.0 |
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- **Related Models:** layoutlm |
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- **Parent Model:** More information needed |
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- **Resources for more information:** |
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- [Associated Paper](https://arxiv.org/abs/1912.13318) |
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- [Blog Post](https://www.impira.com/blog/introducing-instant-invoices) |
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# Uses |
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## Direct Use |
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Text Classification |
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## Downstream Use [Optional] |
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More information needed |
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## Out-of-Scope Use |
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The model should not be used to intentionally create hostile or alienating environments for people. |
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# Bias, Risks, and Limitations |
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Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. |
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## Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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# Training Details |
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## Training Data |
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More information needed |
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## Training Procedure |
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More information needed |
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### Preprocessing |
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More information needed |
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### Speeds, Sizes, Times |
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Num_attention_head: 12 |
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Num_hidden_layer:12, |
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Vocab_size: 30522 |
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# Evaluation |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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More information needed |
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### Factors |
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More information needed |
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### Metrics |
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More information needed |
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## Results |
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More information needed |
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# Model Examination |
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More information needed |
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# Environmental Impact |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** More information needed |
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- **Hours used:** More information needed |
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- **Cloud Provider:** More information needed |
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- **Compute Region:** More information needed |
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- **Carbon Emitted:** More information needed |
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# Technical Specifications [optional] |
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## Model Architecture and Objective |
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More information needed |
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## Compute Infrastructure |
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More information needed |
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### Hardware |
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More information needed |
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### Software |
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Transformers version: 4.4.0.dev0 |
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# Citation |
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**BibTeX:** |
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More information needed} |
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**APA:** |
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More information needed |
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# Glossary [optional] |
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More information needed |
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# More Information [optional] |
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More information needed |
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# Model Card Authors [optional] |
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Impira team in collaboration with Ezi Ozoani and the Hugging Face team. |
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# Model Card Contact |
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More information needed |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-classifier") |
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model = AutoModelForSequenceClassification.from_pretrained("impira/layoutlm-document-classifier") |
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``` |
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</details> |
