NuNER v2.0 EU AI Act Compliance Profile

SOTA Entity Recognition English Foundation Model by NuMind 🔥

This model provides embeddings for the Entity Recognition task in English. It is an improved version of the model from our paper.

Checkout other models by NuMind:

• SOTA Zero-shot NER Model NuNER Zero
• SOTA Multilingual Entity Recognition Foundation Model: link
• SOTA Sentiment Analysis Foundation Model: English, Multilingual

About

Roberta-base fine-tuned on the expanded version of NuNER data using contrastive learning from NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data.

Metrics:

Read more about evaluation protocol & datasets in our NuNER data using contrastive learning from paper.

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

k=X means that as training data, 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. Thus NuNER v2.0 should be even better than the 7b LLM.

| Model | k=8~16| k=64~128 | |----------|----------

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