Instructions to use jinaai/xlm-roberta-flash-implementation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinaai/xlm-roberta-flash-implementation with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jinaai/xlm-roberta-flash-implementation", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update modeling_xlm_roberta_for_glue.py
Browse files
modeling_xlm_roberta_for_glue.py
CHANGED
|
@@ -9,7 +9,7 @@ from .modeling_bert import XLMRobertaPreTrainedModel, XLMRobertaModel
|
|
| 9 |
from .configuration_bert import JinaBertConfig
|
| 10 |
|
| 11 |
|
| 12 |
-
class
|
| 13 |
def __init__(self, config: JinaBertConfig):
|
| 14 |
super().__init__(config)
|
| 15 |
self.num_labels = config.num_labels
|
|
|
|
| 9 |
from .configuration_bert import JinaBertConfig
|
| 10 |
|
| 11 |
|
| 12 |
+
class XLMRobertaForSequenceClassification(XLMRobertaPreTrainedModel):
|
| 13 |
def __init__(self, config: JinaBertConfig):
|
| 14 |
super().__init__(config)
|
| 15 |
self.num_labels = config.num_labels
|