Instructions to use DeepMount00/universal_ner_ita with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use DeepMount00/universal_ner_ita with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("DeepMount00/universal_ner_ita") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -13,4 +13,6 @@ This model is designed for Named Entity Recognition (NER) tasks, specifically ta
|
|
| 13 |
- **Inference Time:** The model runs on CPUs, with an inference time of 0.01 seconds on a GPU. Performance on a CPU will vary depending on the specific hardware configuration.
|
| 14 |
|
| 15 |
## Try It Out
|
| 16 |
-
You can test the model directly in your browser through the following Hugging Face Spaces link: [https://huggingface.co/spaces/DeepMount00/universal_ner](https://huggingface.co/spaces/DeepMount00/universal_ner).
|
|
|
|
|
|
|
|
|
| 13 |
- **Inference Time:** The model runs on CPUs, with an inference time of 0.01 seconds on a GPU. Performance on a CPU will vary depending on the specific hardware configuration.
|
| 14 |
|
| 15 |
## Try It Out
|
| 16 |
+
You can test the model directly in your browser through the following Hugging Face Spaces link: [https://huggingface.co/spaces/DeepMount00/universal_ner](https://huggingface.co/spaces/DeepMount00/universal_ner).
|
| 17 |
+
|
| 18 |
+
It's important to note that **this model is universal and operates across all domains**. However, if you are seeking performance metrics close to a 98/99% F1 score for a specific domain, you are encouraged to reach out via email to Michele Montebovi at montebovi.michele@gmail.com. This direct contact allows for the possibility of customizing the model to achieve enhanced performance tailored to your unique entity recognition requirements in the Italian language.
|