Update README.md

Hi! 👋
This PR has a preliminary model card, based on the format we are using as part of our effort to standardise model cards at Hugging Face. It is generated automatically using our [our tool](https://huggingface.co/spaces/huggingface/Model_Cards_Writing_Tool), as we’re testing our automatic Model Card generation abilities and running a study to see the effects of model cards on models.
Initial evidence suggests that model cards increase usage.
Please take a look when you get a chance, feel free to merge if you are ok with the changes or incorporate any additional information🤗

README.md

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+​
+# Model Card for ivila-row-layoutlm-finetuned-s2vl-v2
+ 
+ 
+ 
+# Model Details
+ 
+## Model Description
+ 
+ 
+- **Developed by:** Allen Institute for AI [allenai]
+- **Shared by [Optional]:** More information needed
+- **Model type:** Token Classification
+- **Language(s) (NLP):** en
+- **License:** More information needed
+- **Parent Model:** [LayoutLM](https://huggingface.co/microsoft/layoutlm-base-uncased)
+- **Resources for more information:** 
+  - [GitHub Repo](https://aka.ms/layoutlm)
+  - [LayoutLM Associated Paper](https://arxiv.org/abs/1912.13318)
+ 	
+ 
+# Uses
+ 
+ 
+## Direct Use
+ 
+This model can be used for the task of document image understanding.
+The [LayoutLM model card](https://huggingface.co/microsoft/layoutlm-base-uncased) notes: 
+ > LayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets.
+​
+## 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)](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.
+ 
+ 
+## 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
+​
+See the [LayoutLM model card](https://huggingface.co/microsoft/layoutlm-base-uncased) for more information
+ 
+>   LayoutLM was pre-trained on IIT-CDIP Test Collection 1.0* dataset with two settings.
+ LayoutLM-Base, Uncased (11M documents, 2 epochs): 12-layer, 768-hidden, 12-heads, 113M parameters (This Model)
+ LayoutLM-Large, Uncased (11M documents, 2 epochs): 24-layer, 1024-hidden, 16-heads, 343M parameters
+ 
+## Training Procedure
+ 
+ 
+### Preprocessing
+ 
+More information needed
+ 
+### Speeds, Sizes, Times
+ 
+More information needed
+ 
+# Evaluation
+ 
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+More information needed
+ 
+### Factors
+ 
+ 
+### 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+ 
+- **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: 4.6.0
+ 
+# Citation
+ 
+ 
+**BibTeX:**
+ ```
+@misc{xu2019layoutlm,
+    title={LayoutLM: Pre-training of Text and Layout for Document Image Understanding},
+    author={Yiheng Xu and Minghao Li and Lei Cui and Shaohan Huang and Furu Wei and Ming Zhou},
+    year={2019},
+    eprint={1912.13318},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+ 
+ 
+# Glossary [optional]
+More information needed
+ 
+# More Information [optional]
+ 
+More information needed
+ 
+# Model Card Authors [optional]
+ 
+ 
+Allen Institute for AI [allenai] 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.
+ 
+<details>
+<summary> Click to expand </summary>
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+ 
+tokenizer = AutoTokenizer.from_pretrained("allenai/ivila-row-layoutlm-finetuned-s2vl-v2")
+ 
+model = AutoModelForTokenClassification.from_pretrained("allenai/ivila-row-layoutlm-finetuned-s2vl-v2")
+```
+</details>