
# Model Card for UniXcoder-base

 
 
# Model Details
 
## Model Description
UniXcoder is a unified cross-modal pre-trained model that leverages multimodal data (i.e. code comment and AST) to pretrain code representation. 
 
- **Developed by:** Microsoft Team 
- **Shared by [Optional]:** Hugging Face
- **Model type:** Feature Engineering
- **Language(s) (NLP):** en
- **License:** Apache-2.0
- **Related Models:**
  - **Parent Model:** RoBERTa
- **Resources for more information:**
    - [Associated Paper](https://arxiv.org/abs/2203.03850)
 
# Uses
 
 
 
## Direct Use
 Feature Engineering
 
 
## Downstream Use [Optional]
 
More information needed
 
## Out-of-Scope Use
 
More information needed
 
# 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
 
The model creators note in the [associated paper](https://arxiv.org/abs/2203.03850):
 
> UniXcoder has slightly worse BLEU-4 scores on both code summarization and generation tasks. The main reasons may come from two aspects. One is the amount of NL-PL pairs in the pre-training data
 
### Metrics
 
The model creators note in the [associated paper](https://arxiv.org/abs/2203.03850):
 
> We evaluate UniXcoder on five tasks over nine public datasets, including two understanding tasks, two generation tasks and an autoregressive task. To further evaluate the performance of code fragment embeddings, we also propose a new task called zero-shot code-to-code search.
 
## Results 
 
The model creators note in the [associated paper](https://arxiv.org/abs/2203.03850):
 
>Taking zero-shot code-code search task as an example, after removing contrastive learning, the performance drops from 20.45% to 13.73%.
 
# 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:**
 ```
@misc{https://doi.org/10.48550/arxiv.2203.03850,
  doi = {10.48550/ARXIV.2203.03850},
  
  url = {https://arxiv.org/abs/2203.03850},
  
  author = {Guo, Daya and Lu, Shuai and Duan, Nan and Wang, Yanlin and Zhou, Ming and Yin, Jian},
  
  keywords = {Computation and Language (cs.CL), Programming Languages (cs.PL), Software Engineering (cs.SE), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {UniXcoder: Unified Cross-Modal Pre-training for Code 
```
 
 
# Glossary [optional]
 
More information needed
 
# More Information [optional]
 
More information needed
 
# Model Card Authors [optional]
 
Microsoft 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.
 
<details>
<summary> Click to expand </summary>

```python
from transformers import AutoTokenizer, AutoModel
 
tokenizer = AutoTokenizer.from_pretrained("microsoft/unixcoder-base")
 
model = AutoModel.from_pretrained("microsoft/unixcoder-base")
 
 ```
</details>

{"language": ["en"], "license": "apache-2.0"}
transformers pytorch roberta feature-extraction en arxiv:2203.03850 arxiv:1910.09700 license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #feature-extraction #en #arxiv-2203.03850 #arxiv-1910.09700 #license-apache-2.0 #endpoints_compatible #has_space #region-us 


# Model Card for UniXcoder-base

 
 
# Model Details
 
## Model Description
UniXcoder is a unified cross-modal pre-trained model that leverages multimodal data (i.e. code comment and AST) to pretrain code representation. 
 
- Developed by: Microsoft Team 
- Shared by [Optional]: Hugging Face
- Model type: Feature Engineering
- Language(s) (NLP): en
- License: Apache-2.0
- Related Models:
  - Parent Model: RoBERTa
- Resources for more information:
    - Associated Paper
 
# Uses
 
 
 
## Direct Use
 Feature Engineering
 
 
## Downstream Use [Optional]
 
More information needed
 
## Out-of-Scope Use
 
More information needed
 
# 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
 
### Preprocessing
 
More information needed
 
### Speeds, Sizes, Times
More information needed
 
# Evaluation
 
 
## Testing Data, Factors & Metrics
 
### Testing Data
More information needed
 
### Factors
 
The model creators note in the associated paper:
 
> UniXcoder has slightly worse BLEU-4 scores on both code summarization and generation tasks. The main reasons may come from two aspects. One is the amount of NL-PL pairs in the pre-training data
 
### Metrics
 
The model creators note in the associated paper:
 
> We evaluate UniXcoder on five tasks over nine public datasets, including two understanding tasks, two generation tasks and an autoregressive task. To further evaluate the performance of code fragment embeddings, we also propose a new task called zero-shot code-to-code search.
 
## Results 
 
The model creators note in the associated paper:
 
>Taking zero-shot code-code search task as an example, after removing contrastive learning, the performance drops from 20.45% to 13.73%.
 
# 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
 
More information needed
 
BibTeX:
 
 
 
# Glossary [optional]
 
More information needed
 
# More Information [optional]
 
More information needed
 
# Model Card Authors [optional]
 
Microsoft 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.
 
<details>
<summary> Click to expand </summary>


</details>

# Model Card for UniXcoder-base # Model Details ## Model Description
UniXcoder is a unified cross-modal pre-trained model that leverages multimodal data (i.e. code comment and AST) to pretrain code representation. 
 
- Developed by: Microsoft Team 
- Shared by [Optional]: Hugging Face
- Model type: Feature Engineering
- Language(s) (NLP): en
- License: Apache-2.0
- Related Models:
  - Parent Model: RoBERTa
- Resources for more information:
    - Associated Paper # Uses ## Direct Use
 Feature Engineering ## Downstream Use [Optional]
 
More information needed ## Out-of-Scope Use
 
More information needed # 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 ### Preprocessing
 
More information needed ### Speeds, Sizes, Times
More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data
More information needed ### Factors
 
The model creators note in the associated paper:
 
> UniXcoder has slightly worse BLEU-4 scores on both code summarization and generation tasks. The main reasons may come from two aspects. One is the amount of NL-PL pairs in the pre-training data ### Metrics
 
The model creators note in the associated paper:
 
> We evaluate UniXcoder on five tasks over nine public datasets, including two understanding tasks, two generation tasks and an autoregressive task. To further evaluate the performance of code fragment embeddings, we also propose a new task called zero-shot code-to-code search. ## Results 
 
The model creators note in the associated paper:
 
>Taking zero-shot code-code search task as an example, after removing contrastive learning, the performance drops from 20.45% to 13.73%. # 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
 
More information needed
 
BibTeX: # Glossary [optional]
 
More information needed # More Information [optional]
 
More information needed # Model Card Authors [optional]
 
Microsoft 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.
 
<details>
<summary> Click to expand </summary>


</details>
TAGS
#transformers #pytorch #roberta #feature-extraction #en #arxiv-2203.03850 #arxiv-1910.09700 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # Model Card for UniXcoder-base # Model Details ## Model Description
UniXcoder is a unified cross-modal pre-trained model that leverages multimodal data (i.e. code comment and AST) to pretrain code representation. 
 
- Developed by: Microsoft Team 
- Shared by [Optional]: Hugging Face
- Model type: Feature Engineering
- Language(s) (NLP): en
- License: Apache-2.0
- Related Models:
  - Parent Model: RoBERTa
- Resources for more information:
    - Associated Paper # Uses ## Direct Use
 Feature Engineering ## Downstream Use [Optional]
 
More information needed ## Out-of-Scope Use
 
More information needed # 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 ### Preprocessing
 
More information needed ### Speeds, Sizes, Times
More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data
More information needed ### Factors
 
The model creators note in the associated paper:
 
> UniXcoder has slightly worse BLEU-4 scores on both code summarization and generation tasks. The main reasons may come from two aspects. One is the amount of NL-PL pairs in the pre-training data ### Metrics
 
The model creators note in the associated paper:
 
> We evaluate UniXcoder on five tasks over nine public datasets, including two understanding tasks, two generation tasks and an autoregressive task. To further evaluate the performance of code fragment embeddings, we also propose a new task called zero-shot code-to-code search. ## Results 
 
The model creators note in the associated paper:
 
>Taking zero-shot code-code search task as an example, after removing contrastive learning, the performance drops from 20.45% to 13.73%. # 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
 
More information needed
 
BibTeX: # Glossary [optional]
 
More information needed # More Information [optional]
 
More information needed # Model Card Authors [optional]
 
Microsoft 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.
 
<details>
<summary> Click to expand </summary>


</details>
passage: TAGS
#transformers #pytorch #roberta #feature-extraction #en #arxiv-2203.03850 #arxiv-1910.09700 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
# Model Card for UniXcoder-base# Model Details## Model Description
UniXcoder is a unified cross-modal pre-trained model that leverages multimodal data (i.e. code comment and AST) to pretrain code representation. 
 
- Developed by: Microsoft Team 
- Shared by [Optional]: Hugging Face
- Model type: Feature Engineering
- Language(s) (NLP): en
- License: Apache-2.0
- Related Models:
  - Parent Model: RoBERTa
- Resources for more information:
    - Associated Paper# Uses## Direct Use
 Feature Engineering## Downstream Use [Optional]
 
More information needed## Out-of-Scope Use
 
More information needed# 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### Preprocessing
 
More information needed### Speeds, Sizes, Times
More information needed# Evaluation## Testing Data, Factors & Metrics### Testing Data
More information needed### Factors
 
The model creators note in the associated paper:
 
> UniXcoder has slightly worse BLEU-4 scores on both code summarization and generation tasks. The main reasons may come from two aspects. One is the amount of NL-PL pairs in the pre-training data