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:
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) 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
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] 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 and HumanEval 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 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
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.
Click to expand
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt-neo-125M-code-clippy-dedup-2048")
model = AutoModelForCausalLM.from_pretrained("flax-community/gpt-neo-125M-code-clippy-dedup-2048")
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