Text Classification
Transformers
PyTorch
English
deberta-v2
reward-model
reward_model
RLHF
text-embeddings-inference
Instructions to use OpenAssistant/reward-model-deberta-v3-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenAssistant/reward-model-deberta-v3-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OpenAssistant/reward-model-deberta-v3-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/reward-model-deberta-v3-large") model = AutoModelForSequenceClassification.from_pretrained("OpenAssistant/reward-model-deberta-v3-large") - Notebooks
- Google Colab
- Kaggle
Python formatting
#2
by lvwerra HF Staff - opened
README.md
CHANGED
|
@@ -34,7 +34,7 @@ All models are train on these dataset with a same split seed across datasets (if
|
|
| 34 |
|
| 35 |
# How to use
|
| 36 |
|
| 37 |
-
```
|
| 38 |
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
| 39 |
reward_name = "OpenAssistant/reward-model-deberta-v3-large"
|
| 40 |
rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name)
|
|
|
|
| 34 |
|
| 35 |
# How to use
|
| 36 |
|
| 37 |
+
```python
|
| 38 |
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
| 39 |
reward_name = "OpenAssistant/reward-model-deberta-v3-large"
|
| 40 |
rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name)
|