Instructions to use softgon/mdeberta-v3-base-multilingual-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use softgon/mdeberta-v3-base-multilingual-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="softgon/mdeberta-v3-base-multilingual-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("softgon/mdeberta-v3-base-multilingual-ner") model = AutoModelForTokenClassification.from_pretrained("softgon/mdeberta-v3-base-multilingual-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,6 +1,9 @@
|
|
| 1 |
---
|
| 2 |
datasets:
|
| 3 |
- mnaguib/WikiNER
|
|
|
|
|
|
|
|
|
|
| 4 |
---
|
| 5 |
# Fine-Tuning mDeBERTa for Named Entity Recognition (NER)
|
| 6 |
|
|
|
|
| 1 |
---
|
| 2 |
datasets:
|
| 3 |
- mnaguib/WikiNER
|
| 4 |
+
license: mit
|
| 5 |
+
pipeline_tag: token-classification
|
| 6 |
+
library_name: adapter-transformers
|
| 7 |
---
|
| 8 |
# Fine-Tuning mDeBERTa for Named Entity Recognition (NER)
|
| 9 |
|