---
tags:
- cnlpt
---

# Model Card for cnlpt-negation-roberta-sharpseed   
 
# Model Details
 
## Model Description

 More information needed
- **Developed by:** More information needed
- **Shared by [Optional]:** Tim Miller
- **Model type:** More information needed
- **Language(s) (NLP):** More information needed
- **License:** More information needed
- **Parent Model:** [RoBERTa](https://huggingface.co/roberta-base?text=Paris+is+the+%3Cmask%3E+of+France.)
- **Resources for more information:** 
 	 - [cnlp GitHub Repo](https://github.com/Machine-Learning-for-Medical-Language/cnlp_transformers)
 
 
 	  



# Uses
 

## Direct Use
More information needed 
 
## 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.

>RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.

See [RoBERTa model card](https://huggingface.co/roberta-base?text=Paris+is+the+%3Cmask%3E+of+France.) for more information.

## 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
 
> The RoBERTa model was pretrained on the reunion of five datasets:
 [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books;
 [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers) ;
[CC-News](https://commoncrawl.org/2016/10/news-dataset-available/), a dataset containing 63 millions English news articles crawled between September 2016 and February 2019.
[OpenWebText](https://github.com/jcpeterson/openwebtext), an opensource recreation of the WebText dataset used to train GPT-2,
[Stories](https://arxiv.org/abs/1806.02847) a dataset containing a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas.

See [RoBERTa model card](https://huggingface.co/roberta-base?text=Paris+is+the+%3Cmask%3E+of+France.) for more information.
 
 
 
 
## 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:**

```bibtex
@article{DBLP:journals/corr/abs-1907-11692,
  author    = {Yinhan Liu and
               Myle Ott and
               Naman Goyal and
               Jingfei Du and
               Mandar Joshi and
               Danqi Chen and
               Omer Levy and
               Mike Lewis and
               Luke Zettlemoyer and
               Veselin Stoyanov},
  title     = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
  journal   = {CoRR},
  volume    = {abs/1907.11692},
  year      = {2019},
  url       = {http://arxiv.org/abs/1907.11692},
  archivePrefix = {arXiv},
  eprint    = {1907.11692},
  timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```




**APA:**

More information needed
  
# Glossary [optional]
 
More information needed

# More Information [optional]
More information needed 

# Model Card Authors [optional]
 
Tim Miller  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 CnlpModelForClassification

model = CnlpModelForClassification.from_pretrained("tmills/cnlpt-negation-roberta-sharpseed")
 ```
</details>