Translation
Transformers
PyTorch
Safetensors
English
t5
text2text-generation
united states air force
united states space force
department of defense
dod
usaf
ussf
afi
air force
space force
bullets
performance reports
evaluations
awards
opr
epr
narratives
interpreter
mbzuai
lamini-flan-t5-783m
flan-t5
google
opera
justinthelaw
text-generation-inference
Inference Endpoints
| language: | |
| - en | |
| license: apache-2.0 | |
| tags: | |
| - united states air force | |
| - united states space force | |
| - department of defense | |
| - dod | |
| - usaf | |
| - ussf | |
| - afi | |
| - air force | |
| - space force | |
| - bullets | |
| - performance reports | |
| - evaluations | |
| - awards | |
| - opr | |
| - epr | |
| - narratives | |
| - interpreter | |
| - translation | |
| - t5 | |
| - mbzuai | |
| - lamini-flan-t5-783m | |
| - flan-t5 | |
| - opera | |
| - justinthelaw | |
| widget: | |
| - text: "Using full sentences, expand upon the following Air and Space Force bullet statement by spelling-out acronyms and adding additional context: - Attended 4-hour EPD Instructor training; taught 3 2-hour Wing EPD & 4 1-hour bullet writing courses--prepared 164 for leadership" | |
| example_title: "Example Usage" | |
| # Opera Bullet Interpreter | |
| An unofficial United States Air Force and Space Force performance statement "translation" model. Takes a properly formatted performance statement, also known as a "bullet," as an input and outputs a long-form sentence, using plain english, describing the accomplishments captured within the bullet. | |
| This checkpoint is a fine-tuned version of the LaMini-Flan-T5-783M, using the justinthelaw/opera-bullet-completions (private) dataset. | |
| To learn more about this project, please visit the [Opera GitHub Repository](https://github.com/justinthelaw/opera). | |
| # Table of Contents | |
| - [Model Details](#model-details) | |
| - [Uses](#uses) | |
| - [Bias, Risks, and Limitations](#bias-risks-and-limitations) | |
| - [Training Details](#training-details) | |
| - [Evaluation](#evaluation) | |
| - [Model Examination](#model-examination) | |
| - [Environmental Impact](#environmental-impact) | |
| - [Technical Specifications](#technical-specifications-optional) | |
| - [Citation](#citation) | |
| - [Model Card Authors](#model-card-authors-optional) | |
| - [Model Card Contact](#model-card-contact) | |
| - [How to Get Started with the Model](#how-to-get-started-with-the-model) | |
| # Model Details | |
| ## Model Description | |
| An unofficial United States Air Force and Space Force performance statement "translation" model. Takes a properly formatted performance statement, also known as a "bullet," as an input and outputs a long-form sentence, using plain english, describing the accomplishments captured within the bullet. | |
| This is a fine-tuned version of the LaMini-Flan-T5-783M, using the justinthelaw/opera-bullet-completions (private) dataset. | |
| - **Developed by:** Justin Law, Alden Davidson, Christopher Kodama, My Tran | |
| - **Model type:** Language Model | |
| - **Language(s) (NLP):** en | |
| - **License:** apache-2.0 | |
| - **Parent Model:** [LaMini-Flan-T5-783M](https://huggingface.co/MBZUAI/LaMini-Flan-T5-783M) | |
| - **Resources for more information:** More information needed | |
| - [GitHub Repo](https://github.com/justinthelaw/opera) | |
| - [Associated Paper](https://huggingface.co/MBZUAI/LaMini-Flan-T5-783M) | |
| # Uses | |
| **_DISCLAIMER_**: Use of the model using Hugging Face's Inference API widget, beside this card, on the Hugging Face website will produce poor results. Please see "[How to Get Started with the Model](#How-to-Get-Started-with-the-Model)" for more details on how to use this model properly. | |
| ## Direct Use | |
| Used to programmatically produce training data for Opera's Bullet Forge (see GitHub repository for details). | |
| The exact prompt to achieve the desired result is: "Using full sentences, expand upon the following Air and Space Force bullet statement by spelling-out acronyms and adding additional context: [INSERT BULLET HERE]" | |
| Below are some examples of the v0.1.0 iteration of this model generating acceptable translations of bullets that it was not previously exposed to during training: | |
| | Bullet | Translation to Sentence | | |
| | :------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | |
| | - Maintained 112 acft G-files; conducted 100% insp of T.Osjob guides--efforts key to flt's 96% LSEP pass rate | Maintained 112 aircraft G-files and conducted 100% inspection of T.O.s job guides, contributing to the flight's 96% LSEP pass rate. | | |
| | - Spearheaded mx for 43 nuke-cert vehs$5.2M; achieved peak 99% MC rt--vital to SECAF #1 prioritynuc deterrence | Spearheaded the maintenance for 43 nuke-cert vehicles worth $5.2 million, achieving a peak 99% mission capability rating. This mission was vital to the Space and Defense Communications System (SECAF) #1 priority nuclear deterrence. | | |
| | - Superb NCO; mng'd mobility ofc during LibyanISAF ops; continuously outshines peers--promote to MSgt now | Superb Non-Commissioned Officer (NCO) who managed the mobility operation during Libyan ISAF operations. I continuously outshines my peers and deserve a promotion to MSgt now. | | |
| | - Managed PMEL prgrm; maintained 300+ essential equipment calibration items--reaped 100% TMDE pass rt | I managed the PMEL program and maintained over 300+ essential equipment calibration items, resulting in a 100% Test, Measurement, and Diagnostic Equipment (TMDE) pass rate. | | |
| ## Downstream Use | |
| Used to quickly interpret bullets written by Airman (Air Force) or Guardians (Space Force), into long-form, plain English sentences. | |
| ## Out-of-Scope Use | |
| Generating bullets from long-form, plain English sentences. General NLP functionality. | |
| # Bias, Risks, and Limitations | |
| Specialized acronyms or abbreviations specific to small units may not be transformed properly. Bullets in highly non-standard formats may result in lower quality results. | |
| ## Recommendations | |
| Look-up acronyms to ensure the correct narrative is being formed. Double-check (spot check) bullets with slightly more complex acronyms and abbreviations for narrative precision. | |
| # Training Details | |
| ## Training Data | |
| The model was fine-tuned on the justinthelaw/opera-bullet-completions dataset, which can be partially found at the GitHub repository. | |
| ## Training Procedure | |
| ### Preprocessing | |
| The justinthelaw/opera-bullet-completions dataset was created using a custom Python web-scraper, along with some custom cleaning functions, all of which can be found at the GitHub repository. | |
| ### Speeds, Sizes, Times | |
| It takes approximately 3-5 seconds per inference when using any standard-sized Air and Space Force bullet statement. | |
| # Evaluation | |
| ## Testing Data, Factors & Metrics | |
| ### Testing Data | |
| 20% of the justinthelaw/opera-bullet-completions dataset was used to validate the model's performance. | |
| ### Factors | |
| Repitition, contextual loss, and bullet format are all loss factors tied into the backward propogation calculations and validation steps. | |
| ### Metrics | |
| ROGUE scores were computed and averaged. These may be provided in future iterations of this model's development. | |
| ## Results | |
| # Model Examination | |
| More information needed | |
| # Environmental Impact | |
| - **Hardware Type:** 2019 MacBook Pro, 16 inch | |
| - **Hours used:** 18 | |
| - **Cloud Provider:** N/A | |
| - **Compute Region:** N/A | |
| - **Carbon Emitted:** N/A | |
| # Technical Specifications | |
| ### Hardware | |
| 2.6 GHz 6-Core Intel Core i7, 16 GB 2667 MHz DDR4, AMD Radeon Pro 5300M 4 GB | |
| ### Software | |
| VSCode, Jupyter Notebook, Python3, PyTorch, Transformers, Pandas, Asyncio, Loguru, Rich | |
| # Citation | |
| **BibTeX:** | |
| ``` | |
| @article{lamini-lm, | |
| author = {Minghao Wu and | |
| Abdul Waheed and | |
| Chiyu Zhang and | |
| Muhammad Abdul-Mageed and | |
| Alham Fikri Aji | |
| }, | |
| title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions}, | |
| journal = {CoRR}, | |
| volume = {abs/2304.14402}, | |
| year = {2023}, | |
| url = {https://arxiv.org/abs/2304.14402}, | |
| eprinttype = {arXiv}, | |
| eprint = {2304.14402} | |
| } | |
| ``` | |
| # Model Card Authors | |
| Justin Law, Alden Davidson, Christopher Kodama, My Tran | |
| # Model Card Contact | |
| Email: [email protected] | |
| # How to Get Started with the Model | |
| Use the code below to get started with the model. | |
| <details> | |
| <summary> Click to expand </summary> | |
| ```python | |
| import torch | |
| from transformers import T5ForConditionalGeneration, T5Tokenizer | |
| bullet_data_creation_prefix = "Using full sentences, expand upon the following Air and Space Force bullet statement by spelling-out acronyms and adding additional context: " | |
| # Path of the pre-trained model that will be used | |
| model_path = "justinthelaw/opera-bullet-interpreter" | |
| # Path of the pre-trained model tokenizer that will be used | |
| # Must match the model checkpoint's signature | |
| tokenizer_path = "justinthelaw/opera-bullet-interpreter" | |
| # Max length of tokens a user may enter for summarization | |
| # Increasing this beyond 512 may increase compute time significantly | |
| max_input_token_length = 512 | |
| # Max length of tokens the model should output for the summary | |
| # Approximately the number of tokens it may take to generate a bullet | |
| max_output_token_length = 512 | |
| # Beams to use for beam search algorithm | |
| # Increased beams means increased quality, but increased compute time | |
| number_of_beams = 6 | |
| # Scales logits before soft-max to control randomness | |
| # Lower values (~0) make output more deterministic | |
| temperature = 0.5 | |
| # Limits generated tokens to top K probabilities | |
| # Reduces chances of rare word predictions | |
| top_k = 50 | |
| # Applies nucleus sampling, limiting token selection to a cumulative probability | |
| # Creates a balance between randomness and determinism | |
| top_p = 0.90 | |
| try: | |
| tokenizer = T5Tokenizer.from_pretrained( | |
| f"{model_path}", | |
| model_max_length=max_input_token_length, | |
| add_special_tokens=False, | |
| ) | |
| input_model = T5ForConditionalGeneration.from_pretrained(f"{model_path}") | |
| logger.info(f"Loading {model_path}...") | |
| # Set device to be used based on GPU availability | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| # Model is sent to device for use | |
| model = input_model.to(device) # type: ignore | |
| input_text = bullet_data_creation_prefix + input("Input a US Air or Space Force bullet: ") | |
| encoded_input_text = tokenizer.encode_plus( | |
| input_text, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=max_input_token_length, | |
| ) | |
| # Generate summary | |
| summary_ids = model.generate( | |
| encoded_input_text["input_ids"], | |
| attention_mask=encoded_input_text["attention_mask"], | |
| max_length=max_output_token_length, | |
| num_beams=number_of_beams, | |
| temperature=temperature, | |
| top_k=top_k, | |
| top_p=top_p, | |
| early_stopping=True, | |
| ) | |
| output_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| # input_text and output_text insert into data sets | |
| print(input_line["output"] + "\n\t" + output_text) | |
| except KeyboardInterrupt: | |
| print("Received interrupt, stopping script...") | |
| except Exception as e: | |
| print(f"An error occurred during generation: {e}") | |
| ``` | |
| </details> | |