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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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#### 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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[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]
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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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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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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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[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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## Model Card Authors [optional]
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[More Information Needed]
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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_medical_finding.recall": 0.9132720105124835,
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"test_medical_finding.word_count": 39191,
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"test_model_preparation_time": 0.0,
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"test_precision": 0.5235630994273097,
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"test_recall": 0.8933364728043325,
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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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"test_therapy.precision": 0.8983204720835225,
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"test_therapy.recall": 0.8934537246049662,
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"test_therapy.word_count": 8813
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