metadata

language:
  - en
license: mit
tags:
  - token-classification
  - entity-recognition
  - foundation-model
  - feature-extraction
  - RoBERTa
  - generic
datasets:
  - numind/NuNER
pipeline_tag: token-classification
inference: false

SOTA Entity Recognition English Foundation Model by NuMind 🔥

This model provides the best embedding for the Entity Recognition task in English. It is an improved version of the model from our paper.

Checkout other models by NuMind:

SOTA Multilingual Entity Recognition Foundation Model: link
SOTA Sentiment Analysis Foundation Model: English, Multilingual

About

Roberta-base fine-tuned on the expanded version of NuNER data using contrastive learning from NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data.

Metrics:

Read more about evaluation protocol & datasets in our NuNER data using contrastive learning from paper.

Here is the aggregated performance of the models over several datasets:

k=X means that as training data, we took only X examples for each class, trained the model, and evaluated it on the full test set.

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 v1.0	39.4	59.6	67.8	71.5
NuNER v2.0	43.6	60.1	68.2	72.0

NuNER v1.0 has similar performance to 7B LLMs (70 times bigger than NuNER v1.0) created specifically for the NER task. Thus NuNER v2.0 should be even better than the 7b LLM.

Model	k=8~16	k=64~128
UniversalNER (7B)	57.89 ± 4.34	71.02 ± 1.53
NuNER v1.0 (100M)	58.75 ± 0.93	70.30 ± 0.35

Usage

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

Get embeddings:

import torch
import transformers


model = transformers.AutoModel.from_pretrained(
    'numind/NuNER-v2.0',
    output_hidden_states=True
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
    'numind/NuNER-v2.0'
)

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)

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}
}