Downloads · 30 days
0
0% of all-time downloads
en_pipeline
nepalprabin/en_pipeline
en_pipeline is a token classification model from nepalprabin. Use it when you need labels on individual words, such as names. It is set up for spacy.
This is a custom named entity recognition model for clinical data. Inorder to see the real usage of the model, \ please enter clinical text in the text field. ---
All-time downloads
5
Public
Repo size
1.3 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
Other659 MB · 52%
At a glance
- Task
- Token Classification
- Library
- spacy
- Access
- Public
- Created
- Mar 8, 2023
- Updated
- Mar 8, 2023
- SHA
- 38c68c60
Try a prompt
Model card
From the Hugging Face model README
This is a custom named entity recognition model for clinical data. Inorder to see the real usage of the model,
please enter clinical text in the text field.
| Feature | Description |
|---|---|
| Name | en_pipeline |
| Version | 0.0.0 |
| spaCy | >=3.5.0,<3.6.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 514157 keys, 514157 unique vectors (300 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
<details> <summary>View label scheme (3 labels for 1 components)</summary>| Component | Labels |
|---|---|
ner | MEDICALCONDITION, MEDICINE, PATHOGEN |
Accuracy
| Type | Score |
|---|---|
ENTS_F | 98.82 |
ENTS_P | 98.82 |
ENTS_R | 98.82 |
TOK2VEC_LOSS | 4597.80 |
NER_LOSS | 29304.32 |
- Task
- Token Classification
- Library
- spacy
- Languages
- en
- Created
- Mar 8, 2023
- Updated
- Mar 8, 2023