
# Welcome to Healthsea ✨
Create better access to health with machine learning and natural language processing. This is the trained healthsea pipeline for analyzing user reviews to supplements by extracting their effects on health. This pipeline features a trained NER model and a custom Text Classification model with Clause Segmentation and Blinding capabilities.

> Read more in the [blog post](https://explosion.ai/blog/healthsea) and visit the [healthsea repository](https://github.com/explosion/healthsea) for all training workflows, custom components and training data.

| Feature | Description |
| --- | --- |
| **Name** | `en_healthsea` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.0,<3.3.0` |
| **Default Pipeline** | `sentencizer`, `tok2vec`, `ner`, `benepar`, `segmentation`, `clausecat`, `aggregation` |
| **Components** | `sentencizer`, `tok2vec`, `ner`, `benepar`, `segmentation`, `clausecat`, `aggregation` |
| **Vectors** | 684830 keys, 684830 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | MIT |
| **Author** | [Explosion](explosion.ai) |

### Label Scheme

<details>

<summary>View label scheme (6 labels for 2 components)</summary>

| Component | Labels |
| --- | --- |
| **`ner`** | `BENEFIT`, `CONDITION` |
| **`clausecat`** | `POSITIVE`, `NEUTRAL`, `NEGATIVE`, `ANAMNESIS` |

</details>

### Accuracy

| Type | Score |
| --- | --- |
| `ENTS_F` | 80.34 |
| `ENTS_P` | 80.77 |
| `ENTS_R` | 79.92 |
| `CATS_SCORE` | 74.87 |
| `CATS_MICRO_P` | 82.17 |
| `CATS_MICRO_R` | 80.85 |
| `CATS_MICRO_F` | 81.51 |
| `CATS_MACRO_P` | 78.01 |
| `CATS_MACRO_R` | 72.41 |
| `CATS_MACRO_F` | 74.87 |
| `CATS_MACRO_AUC` | 92.76 |
| `CATS_LOSS` | 297.22 |

{"language": ["en"], "tags": ["spacy", "token-classification", "text-classification"]}
spacy token-classification text-classification en model-index has_space region:us
TAGS
#spacy #token-classification #text-classification #en #model-index #has_space #region-us 

Welcome to Healthsea
====================


Create better access to health with machine learning and natural language processing. This is the trained healthsea pipeline for analyzing user reviews to supplements by extracting their effects on health. This pipeline features a trained NER model and a custom Text Classification model with Clause Segmentation and Blinding capabilities.



> 
> Read more in the blog post and visit the healthsea repository for all training workflows, custom components and training data.
> 
> 
> 



### Label Scheme



View label scheme (6 labels for 2 components)


### Accuracy




### Label Scheme



View label scheme (6 labels for 2 components) ### Accuracy
TAGS
#spacy #token-classification #text-classification #en #model-index #has_space #region-us 
 ### Label Scheme



View label scheme (6 labels for 2 components) ### Accuracy
passage: TAGS
#spacy #token-classification #text-classification #en #model-index #has_space #region-us 
### Label Scheme



View label scheme (6 labels for 2 components)### Accuracy