Instructions to use hfunakura/en-bertsemtagger-gold with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfunakura/en-bertsemtagger-gold with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hfunakura/en-bertsemtagger-gold")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hfunakura/en-bertsemtagger-gold") model = AutoModelForTokenClassification.from_pretrained("hfunakura/en-bertsemtagger-gold", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -8,7 +8,7 @@ license: apache-2.0
|
|
| 8 |
---
|
| 9 |
# An English semantic tagging model based on `bert-base-uncased`
|
| 10 |
This model is a semantic tagging model created by fine-tuning `bert-base-uncased`.
|
| 11 |
-
As training data, we use the English fragment (gold data only) from the Parallel Meaning Bank's universal semantic tag dataset
|
| 12 |
|
| 13 |
The basic usage is shown below. Note that the model assumes that inputs are already split into words by spaces.
|
| 14 |
```
|
|
|
|
| 8 |
---
|
| 9 |
# An English semantic tagging model based on `bert-base-uncased`
|
| 10 |
This model is a semantic tagging model created by fine-tuning `bert-base-uncased`.
|
| 11 |
+
As training data, we use the English fragment (gold data only) from the Parallel Meaning Bank's universal semantic tag dataset [1], https://pmb.let.rug.nl/data.php.
|
| 12 |
|
| 13 |
The basic usage is shown below. Note that the model assumes that inputs are already split into words by spaces.
|
| 14 |
```
|