Update README.md

README.md

@@ -1,199 +1,233 @@
 ---
 library_name: transformers
-tags: []
+language:
+- code
+license: apache-2.0
 ---
 
-# Model Card for Model ID
+# UniXcoder Base Unimodal
+
+This is an *unofficial* reupload of [microsoft/unixcoder-base-unimodal](https://huggingface.co/microsoft/unixcoder-base-unimodal) in the `SafeTensors` format using `transformers` `4.41.2`. The goal of this reupload is to prevent older models that are still relevant baselines from becoming stale as a result of changes in HuggingFace. Additionally, I may include minor corrections, such as model max length configuration.
+
+## Properties
++-----------------------------------------+
+|          Property          |    Value   |
++----------------------------+------------+
+|    Number Of Parameters    | 124,842,240|
++----------------------------+------------+
+|         Torch Dtype        |   Float32  |
++----------------------------+------------+
+|        Architectures       |RobertaModel|
++----------------------------+------------+
+|        Bos Token Id        |      0     |
++----------------------------+------------+
+|        Pad Token Id        |      1     |
++----------------------------+------------+
+|        Eos Token Id        |      2     |
++----------------------------+------------+
+|    Transformers Version    |   4.41.2   |
++----------------------------+------------+
+|         Model Type         |   Roberta  |
++----------------------------+------------+
+|         Vocab Size         |   50,000   |
++----------------------------+------------+
+|         Hidden Size        |     768    |
++----------------------------+------------+
+|      Num Hidden Layers     |     12     |
++----------------------------+------------+
+|     Num Attention Heads    |     12     |
++----------------------------+------------+
+|         Hidden Act         |    Gelu    |
++----------------------------+------------+
+|      Intermediate Size     |    3,072   |
++----------------------------+------------+
+|     Hidden Dropout Prob    |    0.10    |
++----------------------------+------------+
+|Attention Probs Dropout Prob|    0.10    |
++----------------------------+------------+
+|   Max Position Embeddings  |    1,026   |
++----------------------------+------------+
+|       Type Vocab Size      |     10     |
++----------------------------+------------+
+|      Initializer Range     |    0.02    |
++----------------------------+------------+
+|       Layer Norm Eps       |    0.00    |
++----------------------------+------------+
+|   Position Embedding Type  |  Absolute  |
++-----------------------------------------+
+
+Original model card of `unixcoder-base` below:
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+---
 
+# 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.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
+ 
+ 
+# 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 [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
+ 
+- **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]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+ ```
+@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>