readme: ---
  tags:
  - flax
  ---
  # Model Card for gpt-neo-125M-code-clippy-dedup-2048
   
   
  # Model Details
   
  ## Model Description
  More information needed
   
  - **Developed by:**  Flax Community
  - **Shared by [Optional]:** Hugging Face
  - **Model type:** Text Generation
  - **Language(s) (NLP):** More information needed
  - **License:** More information needed
  - **Related Models:**
    - **Parent Model:** GPT-Neo
  - **Resources for more information:** 
    - [GitHub Repo](https://github.com/CodedotAl/gpt-code-clippy)
   
  # Uses
   
   
  ## Direct Use
   
  This model can be used for the task of Text Generation
   
  ## 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
  The model creators note in the GitHub Repo](https://github.com/CodedotAl/gpt-code-clippy):
  >  **ISSUE : Wrong Filenames in the Dataset**
   We recently came to know about a bug which happened during the scraping of the dataset. We found out that the file names are obsolete/misleading.[Refer this [issue](https://github.com/CodedotAl/gpt-code-clippy/issues/71)] We thank Naman for pointing out the issue.
  This might have two implications
      - Since the filtering for the training dataset is done using the file extension, we might have had wrong datapoints in the dataset while training and we might have missed a lot of right datapoints that belong to the languages of choice. 
      
  # Training Details
   
  ## Training Data
   The model creators note in the GitHub Repo](https://github.com/CodedotAl/gpt-code-clippy):
  > For fine-tuning GPTNeo-125M on CodeClippy dataset we used AdamW optimizer (beta1=0.9, beta2=0.95) with GPT3-like learning rate schedule (4k warmup steps from 0 to 5e-5 followed by 50k cosine decay steps to 5e-6), weight decay 0.1 and batch size 1024, sequence length 2048. 
   
  ## Training Procedure
   
   
  ### Preprocessing
   
  More information needed
   
  ### Speeds, Sizes, Times
   The model creators note in the GitHub Repo](https://github.com/CodedotAl/gpt-code-clippy):
  > For fine-tuning GPTNeo-125M on CodeClippy dataset we used AdamW optimizer (beta1=0.9, beta2=0.95) with GPT3-like learning rate schedule (4k warmup steps from 0 to 5e-5 followed by 50k cosine decay steps to 5e-6), weight decay 0.1 and batch size 1024, sequence length 2048. The choice of relatively large batch size and low LR with long warmup are made to avoid agressive updates and preserve the knowledge contained in pretrained GPTNeo weights.
   
  # Evaluation
   
   
  ## Testing Data, Factors & Metrics
   
  ### Testing Data
   
   
  The model creators note in the GitHub Repo](https://github.com/CodedotAl/gpt-code-clippy):
  > The models are also evaluated on the [APPS](https://github.com/hendrycks/apps) and [HumanEval](https://github.com/openai/human-eval) datasets.
   
  
   
   
   
  ### Factors
   
  More information needed
   
  ### Metrics
   
  More information needed
   
  ## Results 
   
  | Model                             |   pass@1    |   pass@2    |   pass@5    |   pass@10   |
  | --------------------------------- | :---------: | :---------: | :---------: | :---------: |
  | gpt-neo-125M-apps                 |    0.06%    |    0.12%    |    0.30%    |    0.61%    |
   
  # 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
   
  GPTNeoForCausalLM
   
  ## Compute Infrastructure
   
  More information needed
   
  ### Hardware
   
  More information needed
   
  ### Software
  More information needed
   
  # Citation
   
  **BibTeX:**
  More information needed
   
  **APA:**
  More information needed
   
  # Glossary [optional]
   
   
  More information needed
   
  # More Information [optional]
   
  More information needed
   
  # Model Card Authors [optional]
  Flax Community 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, AutoModelForCausalLM
   
  tokenizer = AutoTokenizer.from_p