Token Classification
Transformers
Safetensors
English
roberta
feature-extraction
entity-recognition
foundation-model
RoBERTa
generic
Instructions to use numind/NuNER-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use numind/NuNER-v1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="numind/NuNER-v1.0")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("numind/NuNER-v1.0") model = AutoModel.from_pretrained("numind/NuNER-v1.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -19,7 +19,8 @@ inference: false
|
|
| 19 |
|
| 20 |
This model provides the best embedding for the Entity Recognition task in English.
|
| 21 |
|
| 22 |
-
This
|
|
|
|
| 23 |
|
| 24 |
**Checkout other models by NuMind:**
|
| 25 |
* SOTA Multilingual Entity Recognition Foundation Model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1)
|
|
|
|
| 19 |
|
| 20 |
This model provides the best embedding for the Entity Recognition task in English.
|
| 21 |
|
| 22 |
+
This is the model from our [Paper](https://arxiv.org/abs/2402.15343): NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data
|
| 23 |
+
|
| 24 |
|
| 25 |
**Checkout other models by NuMind:**
|
| 26 |
* SOTA Multilingual Entity Recognition Foundation Model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1)
|