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