README.md

---
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.

 We suggest using **newer version of this model: [NuNER v2.0](https://huggingface.co/numind/NuNER-v2.0)**

 This is the model from our [**Paper**](https://arxiv.org/abs/2402.15343): **NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data**


**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

[Roberta-base](https://huggingface.co/roberta-base) fine-tuned on [NuNER data](https://huggingface.co/datasets/numind/NuNER).

**Metrics:**

Read more about evaluation protocol & datasets in our [paper](https://arxiv.org/abs/2402.15343).

We suggest using **newer version of this model: [NuNER v2.0](https://huggingface.co/numind/NuNER-v2.0)**

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

k=X means that as training data for this evaluation, 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 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** |

NuNER v1.0 has similar performance to 7B LLMs (70 times bigger than NuNER v1.0) created specifically for the NER task.

| 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:


```python
import torch
import transformers


model = transformers.AutoModel.from_pretrained(
    'numind/NuNER-v1.0'
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
    'numind/NuNER-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)

emb = output.last_hidden_state
```

## 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}
}
```