Instructions to use Wanjiru/autotrain_gro_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wanjiru/autotrain_gro_ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Wanjiru/autotrain_gro_ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Wanjiru/autotrain_gro_ner") model = AutoModelForTokenClassification.from_pretrained("Wanjiru/autotrain_gro_ner") - Notebooks
- Google Colab
- Kaggle
Token Classification
Classifies Gro's items and metrics
| tag | token |
|---|---|
| B-ITEM | BEGINNING ITEM |
| I-ITEM | INSIDE ITEM |
| B-METRIC | BEGINNING METRIC |
| I-METRIC | INSIDE METRIC |
| O | OUTSIDE |
Training: Script to train this model
The following Flair script was used to train this model:
from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("Wanjiru/autotrain_gro_ner")
model = AutoModelForTokenClassification.from_pretrained("Wanjiru/autotrain_gro_ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Wanjru"
ner_res = nlp(example)
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