---
tags:
- spacy
- token-classification
- ner
language:
- en
license: mit
model-index:
- name: en_ner_job_postings
  results:
  - task:
      name: NER
      type: token-classification
    metrics:
    - name: NER Precision
      type: precision
      value: 0.8516398746
    - name: NER Recall
      type: recall
      value: 0.8569711538
    - name: NER F Score
      type: f_score
      value: 0.8542971968
  - task:
      name: TAG
      type: token-classification
    metrics:
    - name: TAG (XPOS) Accuracy
      type: accuracy
      value: 0.9734810915
  - task:
      name: UNLABELED_DEPENDENCIES
      type: token-classification
    metrics:
    - name: Unlabeled Attachment Score (UAS)
      type: f_score
      value: 0.9208198801
  - task:
      name: LABELED_DEPENDENCIES
      type: token-classification
    metrics:
    - name: Labeled Attachment Score (LAS)
      type: f_score
      value: 0.9027174273
  - task:
      name: SENTS
      type: token-classification
    metrics:
    - name: Sentences F-Score
      type: f_score
      value: 0.907098331
library_name: spacy
pipeline_tag: text-classification
---
# Custom spaCy NER Model for "Profession," "Facility," and "Experience" Entities

### Overview
This spaCy-based Named Entity Recognition (NER) model has been custom-trained to recognize and classify entities related to "profession," "facility," and "experience." It is designed to enhance your text analysis capabilities by identifying these specific entity types in unstructured text data.

### Key Features
Custom-trained for high accuracy in recognizing "profession," "facility," and "experience" entities.
Suitable for various NLP tasks, such as information extraction, content categorization, and more.
Can be easily integrated into your existing spaCy-based NLP pipelines.

| Feature | Description |
| --- | --- |
| **Name** | `en_ner_job_postings` |
| **Version** | `3.6.0` |
| **spaCy** | `>=3.6.0,<3.7.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
| **License** | `MIT` |



### Usage
Installation
You can install the custom spaCy NER model using pip:

'''
Copy code
pip install your-ner-model-name
Example Usage
Here's how you can use the model for entity recognition in Python:
'''


python
Copy code
import spacy

# Load the custom spaCy NER model
nlp = spacy.load("your-ner-model-name")

# Process your text
text = "John Smith is a software engineer at ABC Corp, with over 10 years of experience."
doc = nlp(text)

# Extract named entities
'for ent in doc.ents:
    print(f"Entity: {ent.text}, Type: {ent.label_}")
Entity Types
The model recognizes the following entity types:

PROFESSION: Represents professions or job titles.
FACILITY: Denotes facilities, buildings, or locations.
EXPERIENCE: Identifies mentions of work experience, durations, or qualifications.
'
### Label Scheme

<details>

<summary>View label scheme (116 labels for 3 components)</summary>

| Component | Labels |
| --- | --- |
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` |
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `EXPERIENCE`, `FAC`, `FACILITY`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `PROFESSION`, `QUANTITY`, `TIME`, `WORK_OF_ART` |

</details>

### Accuracy

| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.86 |
| `TOKEN_P` | 99.57 |
| `TOKEN_R` | 99.58 |
| `TOKEN_F` | 99.57 |
| `TAG_ACC` | 97.35 |
| `SENTS_P` | 92.19 |
| `SENTS_R` | 89.27 |
| `SENTS_F` | 90.71 |
| `DEP_UAS` | 92.08 |
| `DEP_LAS` | 90.27 |
| `ENTS_P` | 85.16 |
| `ENTS_R` | 85.70 |
| `ENTS_F` | 85.43 |