Instructions to use Davlan/bert-base-multilingual-cased-ner-hrl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Davlan/bert-base-multilingual-cased-ner-hrl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Davlan/bert-base-multilingual-cased-ner-hrl")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl") model = AutoModelForTokenClassification.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
TemporalMesh Transformer: 29.4 PPL at 48% compute — beats Mamba, new open-source architecture
#10 opened 2 months ago
by
vigneshwar234
Add tokenizer.json
#9 opened about 1 year ago
by
bu6n
Adding `safetensors` variant of this model
#8 opened almost 2 years ago
by
SFconvertbot
Adding `safetensors` variant of this model
#5 opened almost 3 years ago
by
SFconvertbot
Using with Transformers vs in website
#2 opened almost 4 years ago
by
Ahmed87