A named entity recognition model for chemical entities.
Project description
A named entity recognition model for chemical entities.
Feature | Description |
— | — |
Name | en_chem_ner |
Version | 0.1.0 |
spaCy | >=3.7.5,<3.8.0 |
Default Pipeline | tok2vec, ner |
Components | tok2vec, ner |
Vectors | 0 keys, 0 unique vectors (0 dimensions) |
Sources | n/a |
License | MIT |
Author | [Dinga Wonanke]() |
### Label Scheme
<details>
<summary>View label scheme (1 labels for 1 components)</summary>
Component | Labels |
— | — |
`ner` | CHEMICAL |
</details>
### Accuracy
Type | Score |
— | — |
ENTS_F | 91.45 |
ENTS_P | 91.40 |
ENTS_R | 91.50 |
TOK2VEC_LOSS | 75815.27 |
NER_LOSS | 124867.54 |
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
en_chem_ner-0.1.0.tar.gz
(6.4 MB
view details)
File details
Details for the file en_chem_ner-0.1.0.tar.gz.
File metadata
- Download URL: en_chem_ner-0.1.0.tar.gz
- Upload date:
- Size: 6.4 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.10.8
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
7e859d519f78121d9655ce083160fa78cc55840bfe7df12dd08031121e9ba7e2
|
|
| MD5 |
568678912aa667280fc7a9b0768bbc95
|
|
| BLAKE2b-256 |
3028a4a6674cd247ec4d91a25ff67dfb0704e4afa8ebdefc1aa70b192c8a5bad
|