---
library_name: transformers
tags: [clinical nlp]
---

# Model Card for Model ID

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## Model Details

### Model Description

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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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## Uses

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### Direct Use

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### Out-of-Scope Use

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## Bias, Risks, and 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.

## How to Get Started with the Model

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## Training Details

### Training Data

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### Training Procedure

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

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#### Metrics

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### Results

#### Sensitive Health Information Recognition

| Coding Type | Precision | Recall | F-measure | Support |
|-------------|----------:|:------:|----------:|--------:|
| PATIENT | 0.8187919 | 0.1703911 | 0.2820809 | 716 |
| IDNUM | 0.9224937 | 0.5165094 | 0.6622316 | 2120 |
| DATE | 0.9332247 | 0.9320862 | 0.9326552 | 2459 |
| MEDICALRECORD | 0.7428058 | 0.5528782 | 0.6339217 | 747 |
| CITY | 0.9758308 | 0.8659517 | 0.9176136 | 373 |
| STATE | 0.9937888 | 0.9638554 | 0.9785933 | 332 |
| ZIP | 0.9871795 | 0.6543909 | 0.7870528 | 353 |
| DEPARTMENT | 0.8079096 | 0.6825776 | 0.7399741 | 419 |
| HOSPITAL | 0.9493243 | 0.7036728 | 0.8082454 | 1198 |
| DOCTOR | 0.8871097 | 0.3330328 | 0.4842657 | 3327 |
| TIME | 0.7804878 | 0.4085106 | 0.5363128 | 470 |
| STREET | 0.7372881 | 0.252907 | 0.3766234 | 344 |
| DURATION | 1 | 0.25 | 0.4 | 12 |
| SET | 0.6666667 | 0.4 | 0.5 | 5 |
| AGE | 0.9512195 | 0.7647059 | 0.8478261 | 51 |
| ORGANIZATION | 0.15625 | 0.06756756 | 0.09433962 | 74 |
| LOCATION-OTHER | 0 | 0 | 0 | 6 |
| PHONE | 0 | 0 | 0 | 1 |
| **Micro-avg. F** | 0.9010895 | 0.565926 | 0.695221 | 13007 |
| **Macro-avg. F** | 0.7394651 | 0.4732798 | 0.5771599 | 13007 |

#### Temporal Information Normalization

| Temporal Type | Precision | Recall | F-measure | Support |
|-------------|----------:|:------:|----------:|----------:|
| DATE | 0.7988656 | 0.7446116 | 0.7707851 | 2459 |
| TIME | 0.6875 | 0.2808511 | 0.3987916 | 470 |
| DURATION | 0.6666667 | 0.1666667 | 0.2666667 | 12 |
| SET | 1 | 0.4 | 0.5714286 | 5 |
| **Micro-avg.** | 0.7902772 | 0.667685 | 0.7238271 | 2946 |
| **Macro-avg.** | 0.7882581 | 0.3980323 | 0.5289636 | 2946 |


#### Summary



## Model Examination [optional]

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## Environmental Impact

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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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## Technical Specifications [optional]

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