Token Classification
Transformers
PyTorch
Safetensors
English
bert
bias-detection
social-bias
gus-net
fairness
interpretability
Instructions to use pinthoz/gus-net-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pinthoz/gus-net-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pinthoz/gus-net-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pinthoz/gus-net-bert") model = AutoModelForTokenClassification.from_pretrained("pinthoz/gus-net-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,19 +1,19 @@
|
|
| 1 |
---
|
| 2 |
license: apache-2.0
|
| 3 |
language:
|
| 4 |
-
|
| 5 |
library_name: transformers
|
| 6 |
pipeline_tag: token-classification
|
| 7 |
base_model: bert-base-uncased
|
| 8 |
tags:
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
|
| 15 |
datasets:
|
| 16 |
-
|
| 17 |
metrics:
|
| 18 |
-
|
| 19 |
---
|
|
|
|
| 1 |
---
|
| 2 |
license: apache-2.0
|
| 3 |
language:
|
| 4 |
+
- en
|
| 5 |
library_name: transformers
|
| 6 |
pipeline_tag: token-classification
|
| 7 |
base_model: bert-base-uncased
|
| 8 |
tags:
|
| 9 |
+
- bias-detection
|
| 10 |
+
- social-bias
|
| 11 |
+
- token-classification
|
| 12 |
+
- gus-net
|
| 13 |
+
- fairness
|
| 14 |
+
- interpretability
|
| 15 |
datasets:
|
| 16 |
+
- ethical-spectacle/gus-dataset-v1
|
| 17 |
metrics:
|
| 18 |
+
- f1
|
| 19 |
---
|