Instructions to use remiai3/Named_Entity_Recognition_by_dslim_bert-base-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use remiai3/Named_Entity_Recognition_by_dslim_bert-base-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="remiai3/Named_Entity_Recognition_by_dslim_bert-base-NER")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("remiai3/Named_Entity_Recognition_by_dslim_bert-base-NER", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Named Entity Recognition (CPU/GPU)
- Model:
dslim/bert-base-NER(Apache-2.0) - Task: Extract PER/ORG/LOC/MISC entities.
- Note: Here we just provide the resources for to run this models in the laptops we didn't develop this entire models we just use the open source models for the experiment this model is developed by dslim
Quick start (any project)
# 1) Create env
python -m venv venv && source .venv/bin/activate # Windows: ./venv/Scripts/activate
# 2) Install deps
pip install -r requirements.txt
# 3) Run
python main.py --help
Tip: If you have a GPU + CUDA, PyTorch will auto-use it. If not, everything runs on CPU (slower but works).
Model tree for remiai3/Named_Entity_Recognition_by_dslim_bert-base-NER
Base model
dslim/bert-base-NER