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---
language:
- en
license: mit
tags:
- token-classification
- entity-recognition
- foundation-model
- feature-extraction
- BERT
- generic
datasets:
- numind/NuNER
pipeline_tag: token-classification
inference: false
---
# SOTA Entity Recognition English Foundation Model by NuMind 🔥
This is the **BERT** model from our [**Paper**](https://arxiv.org/abs/2402.15343): **NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data**
<u>**This is the model used in Section 4.2 when comparing against TadNER.**</u>
For other sections, [NuNER v1.0](https://huggingface.co/numind/NuNER-v1.0) is used.
**Checkout other models by NuMind:**
* SOTA Multilingual Entity Recognition Foundation Model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1)
* SOTA Sentiment Analysis Foundation Model: [English](https://huggingface.co/numind/generic-sentiment-v1), [Multilingual](https://huggingface.co/numind/generic-sentiment-multi-v1)
## About
[bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) fine-tuned on [NuNER data](https://huggingface.co/datasets/numind/NuNER).
**Metrics:**
Read more about evaluation protocol datasets in Section 4.2 of our [paper](https://arxiv.org/abs/2402.15343).
## 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-BERT-v1.0',
output_hidden_states=True
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
'numind/NuNER-BERT-v1.0'
)
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)
# 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}
}
``` |