AnanthanarayananSeetharaman/osa-custom-ner-model

My Custom NER Model

This is a custom Named Entity Recognition (NER) model fine-tuned on domain-specific data using a BERT-based architecture.

Entities

The model is trained to recognize the following entities:

• FACT: Facts related to sales, revenue, etc.
• PRDC_CHAR: Product characteristics like product names.
• MRKT_CHAR: Market details like regions.

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Example Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

# Load the model and tokenizer
model = AutoModelForTokenClassification.from_pretrained("AnanthanarayananSeetharaman/osa-custom-ner-model")
tokenizer = AutoTokenizer.from_pretrained("AnanthanarayananSeetharaman/osa-custom-ner-model")

# Create a pipeline
ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)

# Test the pipeline
text = "The SALES of BEER and WINE in TTL US is increasing."
entities = ner_pipeline(text)
print(entities)