Feature Extraction
Transformers
Safetensors
score_predictor
deberta-v3
regression
text-evaluation
multilingual
trust-remote-code
custom_code
Instructions to use QCRI/OmniScore-deberta-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QCRI/OmniScore-deberta-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="QCRI/OmniScore-deberta-v3", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QCRI/OmniScore-deberta-v3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update modeling_score_predictor.py
Browse files
modeling_score_predictor.py
CHANGED
|
@@ -26,7 +26,7 @@ from dataclasses import dataclass, field
|
|
| 26 |
import math
|
| 27 |
|
| 28 |
from .configuration_score_predictor import ScorePredictorConfig
|
| 29 |
-
|
| 30 |
|
| 31 |
@dataclass
|
| 32 |
class ScorePredictorOutput(ModelOutput):
|
|
@@ -593,7 +593,7 @@ class ScorePredictorModel(PreTrainedModel):
|
|
| 593 |
-------
|
| 594 |
ScorePredictorExplainer
|
| 595 |
"""
|
| 596 |
-
|
| 597 |
|
| 598 |
if tokenizer is None:
|
| 599 |
from transformers import AutoTokenizer
|
|
|
|
| 26 |
import math
|
| 27 |
|
| 28 |
from .configuration_score_predictor import ScorePredictorConfig
|
| 29 |
+
from .explain_score_predictor import ScorePredictorExplainer
|
| 30 |
|
| 31 |
@dataclass
|
| 32 |
class ScorePredictorOutput(ModelOutput):
|
|
|
|
| 593 |
-------
|
| 594 |
ScorePredictorExplainer
|
| 595 |
"""
|
| 596 |
+
|
| 597 |
|
| 598 |
if tokenizer is None:
|
| 599 |
from transformers import AutoTokenizer
|