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+ ---
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+ base_model: 'ProtProfileMD'
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+ library_name: peft
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+ tags:
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+ - 'base_model:adapter:'
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+ - lora
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+ - transformers
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ ProtProfileMD
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+
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+
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+
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+ - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** [More Information Needed]
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+ - **License:** MIT
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+ - **Finetuned from model [optional]:** [More Information Needed]
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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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+
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+ ## Uses
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+
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+ <!-- 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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+
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+ ### Direct Use
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+
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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+
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+ [More Information Needed]
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+
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+ ### Downstream Use [optional]
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+
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+ <!-- 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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+
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+ [More Information Needed]
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ [More Information Needed]
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
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+
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+ ### Recommendations
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+
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+
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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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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ [More Information Needed]
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ <!-- 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. -->
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+
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+ [More Information Needed]
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+
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+ ### Training Procedure
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+
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+ <!-- 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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+
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+ #### Preprocessing [optional]
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+
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+ [More Information Needed]
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+
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+
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+ #### Training Hyperparameters
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+
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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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+
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+ #### Speeds, Sizes, Times [optional]
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+
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+
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+ [More Information Needed]
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+
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+ ## Evaluation
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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+ ### Testing Data, Factors & Metrics
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+
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+ #### Testing Data
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+
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+ <!-- This should link to a Dataset Card if possible. -->
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+
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+ [More Information Needed]
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+
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+ #### Factors
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+
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
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+ [More Information Needed]
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+
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+ #### Metrics
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+
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
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+ [More Information Needed]
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+
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+ ### Results
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+
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+ [More Information Needed]
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+
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+ #### Summary
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+
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+
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+
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+ ## Model Examination [optional]
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+
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+ <!-- Relevant interpretability work for the model goes here -->
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+
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+ [More Information Needed]
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+
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+ ## Environmental Impact
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+
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+ <!-- 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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+
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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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+
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+ - **Hardware Type:** [More Information Needed]
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+ - **Hours used:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+
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+ [More Information Needed]
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+
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+ ### Compute Infrastructure
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+
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+ [More Information Needed]
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+
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+ #### Hardware
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+
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+ [More Information Needed]
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+
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+ #### Software
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+
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+ [More Information Needed]
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+
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+ ## Citation [optional]
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+
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+ <!-- 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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+
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+ **BibTeX:**
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+
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+ [More Information Needed]
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+
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+ **APA:**
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+
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+ [More Information Needed]
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+
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+ ## Glossary [optional]
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+
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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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+
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+ [More Information Needed]
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+
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+ ## More Information [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Authors [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Contact
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+
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+ [More Information Needed]
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+ ### Framework versions
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+
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+ - PEFT 0.18.0.rc0
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+ benchmarks:
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+ batch_size: '32'
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+ targets:
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+ - scope
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+ dataset:
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+ directory: ./tmp/data/mdcath/hf_dataset/mdcath_split/
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+ filter_on_column:
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+ replica:
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+ - '0'
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+ - '1'
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+ - '2'
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+ - '3'
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+ - '4'
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+ temperature:
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+ - '320'
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+ metadata:
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+ adapter_name: ProtProfileMD_LoRA
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+ inference: false
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+ log_path: ./tmp/runs/ProtProfileMD_20251113_072928_large_batch_size_again/logs
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+ name: ProtProfileMD
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+ run_id: ProtProfileMD_20251113_072928_large_batch_size_again
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+ version: 1.0
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+ weights: null
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+ model:
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+ base_model: Rostlab/ProstT5
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+ base_model_kwargs:
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+ output_loading_info: false
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+ use_safetensors: true
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+ loss_function: kldiv
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+ loss_function_kwargs:
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+ reduction: batchmean
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+ profile_head: linear
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+ profile_head_kwargs:
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+ dropout: 0.1
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+ hidden_size: 1024
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+ num_classes: 20
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+ tokenizer:
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+ tokenizer_model: Rostlab/ProstT5
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+ tokenizer_model_kwargs:
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+ do_lower_case: false
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+ legacy: false
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+ use_fast: true
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+ training:
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+ data_collator:
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+ pad_to_multiple_of: 8
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+ padding: true
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+ ddp: true
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+ devices: 0,1,2,3
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+ lora:
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+ bias: none
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+ inference_mode: false
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+ lora_alpha: 16
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+ lora_dropout: 0.05
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+ modules_to_save:
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+ - profile_head
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+ r: 8
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+ target_modules:
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+ - q
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+ - v
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+ use_dora: false
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+ use_rslora: false
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+ quantize: null
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+ save_dir: ./tmp/runs/ProtProfileMD_20251113_072928_large_batch_size_again/model
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+ training_args:
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+ batch_eval_metrics: false
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+ eval_on_start: true
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+ eval_steps: 32
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+ eval_strategy: steps
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+ gradient_accumulation_steps: 10
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+ label_names:
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+ - profiles
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+ learning_rate: 0.001
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+ logging_steps: 1
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+ logging_strategy: steps
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+ lr_scheduler_type: cosine
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+ num_train_epochs: 6
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+ output_dir: ./tmp/runs/ProtProfileMD_20251113_072928_large_batch_size_again/training
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+ per_device_eval_batch_size: 32
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+ per_device_train_batch_size: 5
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+ remove_unused_columns: true
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+ report_to: wandb
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+ save_steps: 300
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+ save_strategy: steps
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+ save_total_limit: 16
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+ seed: 42
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+ warmup_steps: 200
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+ wandb:
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+ project: protprofilemd
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training_history.png ADDED