Adding paper details to BERT4RE

README.md

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 library_name: transformers
-tags: []
+tags: [requirements-engineering, domain-specific-lm, concept-extraction]
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-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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+# Model Card for BERT4RE
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+BERT4RE is a domain-specific language model (LM) designed to support various requirements engineering (RE) tasks, including requirements classification, detection of language issues, identification of domain concepts, and establishment of requirements traceability links. BERT4RE is retrained from the generic BERTbase model using publicly available RE-related texts.
 
-- **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]
+- **Developed by:** Muideen Ajagbe, Liping Zhao
+- **Shared by [optional]:** Alberto Rodriguez
+- **Model type:** Domain-specific language model
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** BERTbase
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Paper [optional]:** https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9920081
 
 ## 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. -->
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 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
+BERT4RE can be used directly for various RE tasks such as requirements classification, detection of language issues, and identification of domain concepts from requirements text.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
+BERT4RE can be fine-tuned for specific RE tasks, providing enhanced performance over generic models like BERTbase. An example is its application in a multiclass classification task to identify nine different requirements concepts.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
+The model may not perform well for tasks outside the domain of requirements engineering or on datasets with significantly different characteristics from the training data.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
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 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+Users should be aware of the biases and limitations inherent in the model due to the nature of the training data. Further evaluation and fine-tuning may be necessary for specific applications.
 
 ## 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. -->
+The training data consists of four requirements-related datasets:
 
-[More Information Needed]
+- **PROMISE NFR Dataset:** Contains 625 requirements from 15 software development projects.
+- **PURE Dataset:** Contains 522,444 lexical words and 865,551 tokens, with 29,000 unique words extracted.
+- **App Review Datasets:** Contains four million app reviews with over three million unique words.
+- **Google Playstore App Reviews:** Contains 600,000 app reviews with more than two million unique words.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
+The retraining process follows the procedures used to train the original BERTbase model, involving the Masking LM (MLM) and Next Sentence Prediction (NSP) tasks. The model was retrained on Google Cloud TPU v3-8 with the following hyperparameters:
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
+- **Batch size:** 128 sequences
+- **Tokens per batch:** 3200 tokens (128 * 25 subword tokens)
+- **Total sequence blocks:** 1.3M
+- **Epochs:** 40
+- **Optimization:** Adam with a learning rate of 1e-4, a weight decay of 0.01, and a dropout rate of 0.1
 
 ## Evaluation
+Please see cited paper for evaluation details.
 
-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-[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 -->
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-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).
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-- **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]
+- **Hardware Type:** Google Cloud TPU v3-8
+- **Cloud Provider:** Google Cloud
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+BERT4RE is based on the BERTbase architecture with 12 Transformer blocks, 768 hidden size, and 12 self-attention heads.
 
 ### Compute Infrastructure
 
-[More Information Needed]
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 #### Hardware
 
-[More Information Needed]
+Google Cloud TPU v3-8 with a single 8-core processor.
 
-#### Software
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-[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. -->
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 **BibTeX:**
 
-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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+@INPROCEEDINGS{9920081,
+  author={Ajagbe, Muideen and Zhao, Liping},
+  booktitle={2022 IEEE 30th International Requirements Engineering Conference (RE)}, 
+  title={Retraining a BERT Model for Transfer Learning in Requirements Engineering: A Preliminary Study}, 
+  year={2022},
+  volume={},
+  number={},
+  pages={309-315},
+  keywords={Deep learning;Bit error rate;Transfer learning;Natural language processing;Requirements engineering;Task analysis;Requirements Engineering;Requirements Classification;Language Models;BERT;Domain-Specific Language Models;Transfer Learning;Deep Learning;Machine Learning;Natural Language Processing},
+  doi={10.1109/RE54965.2022.00046}}
+  
 ## Model Card Authors [optional]
-
-[More Information Needed]
+Muideen Ajagbe, Liping Zhao
 
 ## Model Card Contact
 
-[More Information Needed]
+Alberto Rodriguez (arodri39@nd.edu)