Instructions to use UBC-NLP/prags2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/prags2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="UBC-NLP/prags2", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/prags2") model = AutoModelForMaskedLM.from_pretrained("UBC-NLP/prags2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
02b42c6
1
Parent(s): fec77c5
Update README.md
Browse files
README.md
CHANGED
|
@@ -4,6 +4,15 @@ license: cc-by-nc-3.0
|
|
| 4 |
|
| 5 |
PragS2: Pragmatic Masked Language Modeling with Emoji_any dataset followed by Hashtag-Based Surrogate Fine-Tuning
|
| 6 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 7 |
More details are in our paper:
|
| 8 |
```
|
| 9 |
@inproceedings{zhang-abdul-mageed-2022-improving,
|
|
|
|
| 4 |
|
| 5 |
PragS2: Pragmatic Masked Language Modeling with Emoji_any dataset followed by Hashtag-Based Surrogate Fine-Tuning
|
| 6 |
|
| 7 |
+
You can load these model and use for downstream fine-tuning. For example:
|
| 8 |
+
```python
|
| 9 |
+
from transformers import AutoTokenizer, AutoModelForSequenceClassification
|
| 10 |
+
|
| 11 |
+
tokenizer = AutoTokenizer.from_pretrained('UBC-NLP/prags1', use_fast = True)
|
| 12 |
+
model = AutoModelForSequenceClassification.from_pretrained('UBC-NLP/prags1',num_labels=lable_size)
|
| 13 |
+
```
|
| 14 |
+
|
| 15 |
+
|
| 16 |
More details are in our paper:
|
| 17 |
```
|
| 18 |
@inproceedings{zhang-abdul-mageed-2022-improving,
|