model documentation

README.md

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+---
+tags:
+- feature-extraction
+---
+# Model Card for code_trans_t5_large_code_comment_generation_java_transfer_learning_finetune 
+ 
+ 
+# Model Details
+ 
+## Model Description
+ 
+ 
+- **Developed by:** Sebis (Software Engineering for Business Information Systems )
+- **Shared by [Optional]:** More information needed
+- **Model type:** Feature Extraction
+- **Language(s) (NLP):** More information needed
+- **License:** More information needed
+- **Related Models:** [All T5 Checkpoints](https://huggingface.co/models?search=t5)
+  - **Parent Model:** T5
+- **Resources for more information:**
+ 	 - [Associated Paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf)
+ 	 - [Blog Post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
+ 
+# Uses
+ 
+ 
+## Direct Use
+ 
+This model can be used for the task of Feature Extraction
+ 
+## 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
+ 
+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 model is pre-trained on the [Colossal Clean Crawled Corpus (C4)](https://www.tensorflow.org/datasets/catalog/c4), which was developed and released in the context of the same [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) as T5.
+ 
+The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**.
+ 
+ See the [t5-base model card](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) for further information.
+ 
+## Training Procedure
+ 
+ 
+### Preprocessing
+ 
+More information needed
+ 
+### Speeds, Sizes, Times
+ 
+More information needed
+ 
+# Evaluation
+ 
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+More information needed
+ 
+### Factors
+ 
+ 
+### 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{2020t5,
+  author  = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
+  title   = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
+  journal = {Journal of Machine Learning Research},
+  year    = {2020},
+  volume  = {21},
+  number  = {140},
+  pages   = {1-67},
+  url     = {http://jmlr.org/papers/v21/20-074.html}
+}
+```
+ 
+ 
+**APA:**
+```
+- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67.
+nformation needed
+```
+ 
+# Glossary [optional]
+More information needed
+ 
+# More Information [optional]
+ 
+More information needed
+ 
+# Model Card Authors [optional]
+ 
+ 
+Sebis (Software Engineering for Business Information Systems ) 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("SEBIS/code_trans_t5_large_code_comment_generation_java_transfer_learning_finetune")
+ 
+model = AutoModel.from_pretrained("SEBIS/code_trans_t5_large_code_comment_generation_java_transfer_learning_finetune")
+ 
+```
+</details>
+
+