---
tags:
- fill-mask
- deberta
---

# Model Card for patentdeberta_base_spec_1024_pwi 
 
# Model Details
 
## Model Description
 
More information needed
 
- **Developed by:** More information needed
- **Shared by [Optional]:** tanapatentlm
- **Model type:** Fill Mask
- **Language(s) (NLP):** More information needed
- **License:** More information needed
- **Parent Model:** [DeBERTa](https://huggingface.co/microsoft/deberta-base?text=The+goal+of+life+is+%5BMASK%5D.)
- **Resources for more information:** More information needed


# Uses
 

## Direct Use
This model can be used for the task of Fill Mask. 
 
## 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
 
More information needed 
 
## Training Procedure

 
### Preprocessing
 
More information needed 


 
### Speeds, Sizes, Times
 
More information needed 


 
# 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](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
 
More information needed.
 
# Citation

 
**BibTeX:**
 
More information needed
```bibtex 
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```


**APA:**

More information needed
  
# Glossary [optional]
 
More information needed

# More Information [optional]
More information needed 

# Model Card Authors [optional]
 
Tanapatentlm 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, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("tanapatentlm/patentdeberta_base_spec_1024_pwi")

model = AutoModelForMaskedLM.from_pretrained("tanapatentlm/patentdeberta_base_spec_1024_pwi")
 ```
</details>