update REAMDE

README.md

@@ -40,16 +40,16 @@ This spaCy-based Named Entity Recognition (NER) model has been custom-trained to
 
 ### 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.
+Suitable for various professional info streams tasks, such as information extraction, content categorization, and more.
+Currently Focus on the job seekers fields, can be easily integrated into your existing spaCy-based NLP pipelines.
 
 ### Usage
 #### Installation
 ##### You can install the custom spaCy NER model using pip:
 
 ```bash
-pip install https://huggingface.co/DaFull/en_core_web_sm_job/resolve/main/en_core_web_sm_job-any-py3-none-any.whl
-
+git lfs install
+git clone https://huggingface.co/LPDoctor/en_core_web_sm_job_related
 ```
 #### Example Usage
 Here's how you can use the model for entity recognition in Python:
@@ -59,7 +59,7 @@ Here's how you can use the model for entity recognition in Python:
 import spacy
 
 # Load the custom spaCy NER model
-nlp = spacy.load("en_core_web_sm_job ")
+nlp = spacy.load("en_core_web_sm_job")
 
 # Process your text
 text = "HR Specialist needed at Google, Dallas, TX, with expertise in employee relations and a minimum of 4 years of HR experience."
@@ -80,12 +80,13 @@ The model recognizes the following entity types:
 
 | Feature | Description |
 | --- | --- |
-| **Name** | `en_core_web_sm_job ` |
-| **Version** | `3.6.0` |
-| **spaCy** | `>=3.6.0,<3.7.0` |
+| **Name** | `en_core_web_sm_job` |
+| **Version** | `3.7.0` |
+| **spaCy** | `>=3.7.0,<3.8.0` |
 | **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) |
+| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
+| **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br />[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br />[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University) |
 | **License** | `MIT` |
 
 
@@ -107,7 +108,7 @@ The model recognizes the following entity types:
 
 | Type | Score |
 | --- | --- |
-| `TOKEN_P` | 75.57 |
-| `TOKEN_R` | 60.58 |
-| `TOKEN_F` | 67.57 |
-| `CUSTOM_TAG_ACC` | 73.35 |
+| `TOKEN_P` | 78.59 |
+| `TOKEN_R` | 63.58 |
+| `TOKEN_F` | 70.57 |
+| `CUSTOM_TAG_ACC` | 71.98 |