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  ---
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  library_name: transformers
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- tags: []
 
 
 
 
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  ---
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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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- ## 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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  ---
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  library_name: transformers
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+ language:
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+ - de
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+ base_model:
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+ - deepset/gbert-base
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+ pipeline_tag: token-classification
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  ---
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  # Model Card for Model ID
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+ We fine-tuned our base model on the Ca dataset.
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+
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+
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+ ## Metrics
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+
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+ seqeval entity-wise in evaulate
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+
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+ _train_AVGf10.9756326545937595
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+ _train_DIAGNOSIS.avg_tokens_per_entity6.812357501139991
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+ _train_DIAGNOSIS.entity_count8772
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+ _train_DIAGNOSIS.f10.9847202499289974
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+ _train_DIAGNOSIS.precision0.9813200498132005
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+ _train_DIAGNOSIS.recall0.9881440948472412
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+ _train_DIAGNOSIS.token_count59758
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+ _train_DIAGNOSTIC.avg_tokens_per_entity5.99338106173173
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+ _train_DIAGNOSTIC.entity_count7403
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+ _train_DIAGNOSTIC.f10.9729585006693441
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+ _train_DIAGNOSTIC.precision0.9643094069258326
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+ _train_DIAGNOSTIC.recall0.9817641496690531
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+ _train_DIAGNOSTIC.token_count44369
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+ _train_DRUG.avg_tokens_per_entity3.8747056052755533
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+ _train_DRUG.entity_count4246
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+ _train_DRUG.f10.9928328046058043
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+ _train_DRUG.precision0.9906213364595545
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+ _train_DRUG.recall0.9950541686292982
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+ _train_DRUG.token_count16452
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+ _train_MEDICAL_FINDING.avg_tokens_per_entity8.822587975587586
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+ _train_MEDICAL_FINDING.entity_count30804
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+ _train_MEDICAL_FINDING.f10.9658010684140024
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+ _train_MEDICAL_FINDING.precision0.9603299419071156
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+ _train_MEDICAL_FINDING.recall0.9713348915725231
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+ _train_MEDICAL_FINDING.token_count271771
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+ _train_THERAPY.avg_tokens_per_entity8.560322448421916
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+ _train_THERAPY.entity_count7319
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+ _train_THERAPY.f10.9618506493506493
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+ _train_THERAPY.precision0.9524447421299397
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+ _train_THERAPY.recall0.9714441863642574
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+ _train_THERAPY.token_count62653
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+ _train_accuracy0.9938551197147224
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+ _train_f10.9709934550640488
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+ _train_loss0.020609384402632713
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+ _train_precision0.9651517964122382
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+ _train_recall0.9769062585405849
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+ _train_runtime142.8691
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+ _train_samples_per_second229.063
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+ _train_steps_per_second28.635
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+ eval_AVGf10.7889642398534424
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+ eval_DIAGNOSIS.avg_len6.790370685982105
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+ eval_DIAGNOSIS.avg_tokens_per_entity6.790370685982105
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+ eval_DIAGNOSIS.count2347
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+ eval_DIAGNOSIS.entity_count2347
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+ eval_DIAGNOSIS.f10.7870941224825319
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+ eval_DIAGNOSIS.precision0.760222310440651
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+ eval_DIAGNOSIS.recall0.815935236472092
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+ eval_DIAGNOSIS.token_count15937
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+ eval_DIAGNOSTIC.avg_len6.030130756111427
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+ eval_DIAGNOSTIC.avg_tokens_per_entity6.030130756111427
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+ eval_DIAGNOSTIC.count1759
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+ eval_DIAGNOSTIC.entity_count1759
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+ eval_DIAGNOSTIC.f10.7870518994114499
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+ eval_DIAGNOSTIC.precision0.7433046993431026
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+ eval_DIAGNOSTIC.recall0.8362706083001705
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+ eval_DIAGNOSTIC.token_count10607
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+ eval_DRUG.avg_len3.9235500878734624
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+ eval_DRUG.avg_tokens_per_entity3.9235500878734624
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+ eval_DRUG.count1138
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+ eval_DRUG.entity_count1138
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+ eval_DRUG.f10.9196581196581196
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+ eval_DRUG.precision0.8951747088186356
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+ eval_DRUG.recall0.945518453427065
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+ eval_DRUG.token_count4465
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+ eval_MEDICAL_FINDING.avg_len8.781120867768594
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+ eval_MEDICAL_FINDING.avg_tokens_per_entity8.781120867768594
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+ eval_MEDICAL_FINDING.count7744
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+ eval_MEDICAL_FINDING.entity_count7744
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+ eval_MEDICAL_FINDING.f10.7699975080986794
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+ eval_MEDICAL_FINDING.precision0.7438613384689456
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+ eval_MEDICAL_FINDING.recall0.7980371900826446
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+ eval_MEDICAL_FINDING.token_count68001
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+ eval_THERAPY.avg_len8.44420941300899
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+ eval_THERAPY.avg_tokens_per_entity8.44420941300899
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+ eval_THERAPY.count1891
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+ eval_THERAPY.entity_count1891
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+ eval_THERAPY.f10.6810195496164316
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+ eval_THERAPY.precision0.64
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+ eval_THERAPY.recall0.7276573241671074
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+ eval_THERAPY.token_count15968
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+ eval_accuracy0.9332097564796261
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+ eval_f10.7744305184135064
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+ eval_loss0.5050501823425293
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+ eval_precision0.7437801708132195
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+ eval_recall0.8077155722830835
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+ eval_runtime36.8437
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+ eval_samples_per_second222.073
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+ eval_steps_per_second27.766
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+ test_AVGf10.7491200818619402
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+ test_DIAGNOSIS.avg_len7.408243727598566
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+ test_DIAGNOSIS.avg_tokens_per_entity7.408243727598566
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+ test_DIAGNOSIS.count2790
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+ test_DIAGNOSIS.entity_count2790
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+ test_DIAGNOSIS.f10.703534151254349
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+ test_DIAGNOSIS.precision0.7192062897791089
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+ test_DIAGNOSIS.recall0.6885304659498208
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+ test_DIAGNOSIS.token_count20669
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+ test_DIAGNOSTIC.avg_len6.136954503249767
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+ test_DIAGNOSTIC.avg_tokens_per_entity6.136954503249767
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+ test_DIAGNOSTIC.count2154
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+ test_DIAGNOSTIC.entity_count2154
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+ test_DIAGNOSTIC.f10.7718579234972678
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+ test_DIAGNOSTIC.precision0.7573726541554959
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+ test_DIAGNOSTIC.recall0.786908077994429
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+ test_DIAGNOSTIC.token_count13219
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+ test_DRUG.avg_len3.7937931034482757
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+ test_DRUG.avg_tokens_per_entity3.7937931034482757
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+ test_DRUG.count1450
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+ test_DRUG.entity_count1450
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+ test_DRUG.f10.9024472008045592
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+ test_DRUG.precision0.878016960208741
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+ test_DRUG.recall0.9282758620689655
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+ test_DRUG.token_count5501
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+ test_MEDICAL_FINDING.avg_len9.53191489361702
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+ test_MEDICAL_FINDING.avg_tokens_per_entity9.53191489361702
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+ test_MEDICAL_FINDING.count8366
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+ test_MEDICAL_FINDING.entity_count8366
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+ test_MEDICAL_FINDING.f10.7280362842264404
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+ test_MEDICAL_FINDING.precision0.6848203939745076
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+ test_MEDICAL_FINDING.recall0.7770738704279225
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+ test_MEDICAL_FINDING.token_count79744
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+ test_THERAPY.avg_len8.884771802982376
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+ test_THERAPY.avg_tokens_per_entity8.884771802982376
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+ test_THERAPY.count2213
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+ test_THERAPY.entity_count2213
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+ test_THERAPY.f10.639724849527085
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+ test_THERAPY.precision0.6100861008610086
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+ test_THERAPY.recall0.6723904202440126
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+ test_THERAPY.token_count19662
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+ test_accuracy0.9229989726085077
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+ test_f10.7327920332701502
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+ test_loss0.6381183862686157
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+ test_precision0.7048546859693045
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+ test_recall0.7630354091792847
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+ test_runtime42.7477
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+ test_samples_per_second221.977
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+ test_steps_per_second27.768