SOTA Entity Recognition Multilingual Foundation Model by NuMind 🔥

This model provides the best embedding for the Entity Recognition task and supports 9+ languages.

Checkout other models by NuMind:

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

About

Multilingual BERT finetunned on an artificially annotated multilingual subset of Oscar dataset. This model provides domain & language independent embedding for Entity Recognition Task. We fine-tunned it only on 9 languages but the model can generalize over other languages that are supported by the Multilingual BERT.

Metrics:

Read more about evaluation protocol & datasets in our blog post

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

text = [
 "NuMind is an AI company based in Paris and USA.",
 "NuMind est une entreprise d'IA basée à Paris et aux États-Unis.",
 "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)

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

# single emb: 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}
}