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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 Details
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- ### Model Description
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- <!-- 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.
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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:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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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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- ## Uses
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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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- ### Direct Use
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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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- [More Information Needed]
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- ### Downstream Use [optional]
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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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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- 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
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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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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- [More Information Needed]
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- ### Training Procedure
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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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- #### 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]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- 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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- ### Results
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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]
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- ## Environmental Impact
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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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- 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]
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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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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- [More Information Needed]
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- ## Citation [optional]
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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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- **BibTeX:**
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- **APA:**
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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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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
 
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  # Model Card for Model ID
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+ "eval_AVGf1": 0.9223289834840258,
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+ "eval_accuracy": 0.9272914758360438,
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+ "eval_diagnosis.avg_words_per_entity": 2.2245762711864407,
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+ "eval_diagnosis.entity_count": 2360,
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+ "eval_diagnosis.f1": 0.8788986878898688,
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+ "eval_diagnosis.precision": 0.8925294888597641,
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+ "eval_diagnosis.recall": 0.8656779661016949,
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+ "eval_diagnosis.word_count": 5250,
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+ "eval_diagnostic.avg_words_per_entity": 1.8057921635434413,
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+ "eval_diagnostic.entity_count": 1761,
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+ "eval_diagnostic.f1": 0.9564464955292761,
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+ "eval_diagnostic.precision": 0.9718640093786636,
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+ "eval_diagnostic.recall": 0.9415105053946621,
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+ "eval_diagnostic.word_count": 3180,
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+ "eval_drug.avg_words_per_entity": 1.0905096660808435,
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+ "eval_drug.entity_count": 1138,
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+ "eval_drug.f1": 0.957187922487607,
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+ "eval_drug.precision": 0.9824236817761333,
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+ "eval_drug.recall": 0.9332161687170475,
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+ "eval_drug.word_count": 1241,
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+ "eval_f1": 0.6559352257940142,
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+ "eval_loss": 0.005444246344268322,
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+ "eval_medical_finding.avg_words_per_entity": 4.152033985581874,
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+ "eval_medical_finding.entity_count": 7768,
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+ "eval_medical_finding.f1": 0.9184775620419185,
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+ "eval_medical_finding.precision": 0.917415874646802,
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+ "eval_medical_finding.recall": 0.9195417095777549,
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+ "eval_medical_finding.word_count": 32253,
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+ "eval_model_preparation_time": 0.0,
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+ "eval_precision": 0.5121161950632149,
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+ "eval_recall": 0.9120761292052004,
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+ "eval_runtime": 311.6758,
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+ "eval_samples_per_second": 26.252,
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+ "eval_steps_per_second": 6.565,
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+ "eval_therapy.avg_words_per_entity": 3.6986807387862797,
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+ "eval_therapy.entity_count": 1895,
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+ "eval_therapy.f1": 0.9006342494714588,
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+ "eval_therapy.precision": 0.9020645844362096,
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+ "eval_therapy.recall": 0.8992084432717679,
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+ "eval_therapy.word_count": 7009,
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+ "test_AVGf1": 0.8991844061835648,
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+ "test_accuracy": 0.9393252816821795,
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+ "test_diagnosis.avg_words_per_entity": 2.540414878397711,
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+ "test_diagnosis.entity_count": 2796,
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+ "test_diagnosis.f1": 0.7905454545454544,
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+ "test_diagnosis.precision": 0.8039940828402367,
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+ "test_diagnosis.recall": 0.7775393419170243,
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+ "test_diagnosis.word_count": 7103,
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+ "test_diagnostic.avg_words_per_entity": 1.9772727272727273,
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+ "test_diagnostic.entity_count": 2156,
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+ "test_diagnostic.f1": 0.9380572501173158,
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+ "test_diagnostic.precision": 0.9491927825261158,
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+ "test_diagnostic.recall": 0.9271799628942486,
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+ "test_diagnostic.word_count": 4263,
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+ "test_drug.avg_words_per_entity": 1.033793103448276,
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+ "test_drug.entity_count": 1450,
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+ "test_drug.f1": 0.9711267605633803,
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+ "test_drug.precision": 0.9920863309352518,
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+ "test_drug.recall": 0.9510344827586207,
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+ "test_drug.word_count": 1499,
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+ "test_f1": 0.6601992430504198,
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+ "test_loss": 0.005932590924203396,
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+ "test_medical_finding.avg_words_per_entity": 4.681758451797873,
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+ "test_medical_finding.entity_count": 8371,
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+ "test_medical_finding.f1": 0.900312076782665,
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+ "test_medical_finding.precision": 0.8877148165350673,
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+ "test_precision": 0.5235630994273097,
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+ "test_runtime": 357.9554,
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+ "test_samples_per_second": 26.509,
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+ "test_steps_per_second": 6.629,
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+ "test_therapy.avg_words_per_entity": 3.9787810383747177,
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+ "test_therapy.entity_count": 2215,
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+ "test_therapy.f1": 0.8958804889090086,
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