metadata

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

Entity Recognition English Foundation Model by NuMind 🔥

This model provides great token embedding for the Entity Recognition task in English.

We suggest using newer version of this model: NuNER v2.0

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 NuNER data.

Metrics:

Read more about evaluation protocol & datasets in our paper and blog post.

Model	F1 macro
RoBERTa-base	0.7129
ours	0.7500
ours + two emb	0.7686

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-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}
}