Instructions to use Azma-AI/bert-uncased-keyword-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Azma-AI/bert-uncased-keyword-extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Azma-AI/bert-uncased-keyword-extractor")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Azma-AI/bert-uncased-keyword-extractor") model = AutoModelForTokenClassification.from_pretrained("Azma-AI/bert-uncased-keyword-extractor") - Notebooks
- Google Colab
- Kaggle
bert-uncased-keyword-extractor
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1247
- Precision: 0.8547
- Recall: 0.8825
- Accuracy: 0.9741
- F1: 0.8684
Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="Azma-AI/bert-uncased-keyword-extractor")
Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Azma-AI/bert-uncased-keyword-extractor")
model = AutoModelForTokenClassification.from_pretrained("Azma-AI/bert-uncased-keyword-extractor")
- Downloads last month
- 7