DFKI-SLT/few-nerd
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How to use huggingsaurusRex/bert-base-uncased-for-mountain-ner with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="huggingsaurusRex/bert-base-uncased-for-mountain-ner") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("huggingsaurusRex/bert-base-uncased-for-mountain-ner")
model = AutoModelForTokenClassification.from_pretrained("huggingsaurusRex/bert-base-uncased-for-mountain-ner", device_map="auto")Detect mountain names in text using token classification.
from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
# Load model and tokenizer
model = AutoModelForTokenClassification.from_pretrained('huggingsaurusRex/bert-base-uncased-for-mountain-ner')
tokenizer = AutoTokenizer.from_pretrained('huggingsaurusRex/bert-base-uncased-for-mountain-ner')
# Create NER pipeline
ner = pipeline('ner', model=model, tokenizer=tokenizer)
# Perform inference
res = ner("I spent days climbing the Mount Everest.")
print(res)
The model is a BERT-based token classification model fine-tuned on the Few-NERD dataset.