Model	k=1	k=4	k=16	k=64
RoBERTa-base	24.5	44.7	58.1	65.4
RoBERTa-base + NER-BERT pre-training	32.3	50.9	61.9	67.6
NuNER v0.1	34.3	54.6	64.0	68.7
NuNER v1.0	39.4	59.6	67.8	71.5
NuNER v2.0	43.6	61.0	68.2	72.0

Usage

Embeddings can be used out of the box or fine-tuned on specific datasets.

Get embeddings:

python

import torch
import transformers


model = transformers.AutoModel.from_pretrained(
    'numind/NuNER-v0.1',
    output_hidden_states=True
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
    'numind/NuNER-v0.1'
)

text = [
    "NuMind is an AI company based in Paris and USA.",
    "See other models from us on https://huggingface.co/numind"
]
encoded_input = tokenizer(
    text,
    return_tensors='pt',
    padding=True,
    truncation=True
)
output = model(**encoded_input)

# for better quality
emb = torch.cat(
    (output.hidden_states[-1], output.hidden_states[-7]),
    dim=2
)

# for better speed
# emb = output.hidden_states[-1]

Citation

@misc{bogdanov2024nuner,
      title={NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data}, 
      author={Sergei Bogdanov and Alexandre Constantin and Timothée Bernard and Benoit Crabbé and Etienne Bernard},
      year={2024},
      eprint={2402.15343},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}