Instructions to use Dudeman523/RoBERTa_ner_plant_names with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dudeman523/RoBERTa_ner_plant_names with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Dudeman523/RoBERTa_ner_plant_names")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Dudeman523/RoBERTa_ner_plant_names") model = AutoModelForTokenClassification.from_pretrained("Dudeman523/RoBERTa_ner_plant_names") - Notebooks
- Google Colab
- Kaggle
RoBERTa Plant NER Model
Fine-tuned RoBERTa model for plant entity recognition.
Labels
O: Outside entityB-PLANT_COMMON: Beginning of common plant nameI-PLANT_COMMON: Inside common plant nameB-PLANT_SCI: Beginning of scientific plant nameI-PLANT_SCI: Inside scientific plant name
Usage
Python
from transformers import pipeline
classifier = pipeline("token-classification", "Dudeman523/roberta-plant-ner-fixed")
result = classifier("I have a Rosa damascena plant")
JavaScript (transformers.js)
import { pipeline } from '@xenova/transformers';
const classifier = await pipeline('token-classification', 'Dudeman523/roberta-plant-ner-fixed');
const result = await classifier('I have a Rosa damascena plant');
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