Text Classification
Transformers
PyTorch
Safetensors
English
bert
reward model
alignment
preference model
RLHF
text-embeddings-inference
Instructions to use nicholasKluge/RewardModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nicholasKluge/RewardModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nicholasKluge/RewardModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/RewardModel") model = AutoModelForSequenceClassification.from_pretrained("nicholasKluge/RewardModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
8d0ac59
1
Parent(s): 1c00037
Update README.md
Browse files
README.md
CHANGED
|
@@ -79,7 +79,7 @@ tokens = tokenizer(question, response1,
|
|
| 79 |
tokens.to(device)
|
| 80 |
|
| 81 |
# Score the response
|
| 82 |
-
score =
|
| 83 |
|
| 84 |
print(f"Question: {question} \n")
|
| 85 |
print(f"Response 1: {response1} Score: {score:.3f}")
|
|
@@ -91,7 +91,7 @@ tokens = tokenizer(question, response2,
|
|
| 91 |
|
| 92 |
tokens.to(device)
|
| 93 |
|
| 94 |
-
score =
|
| 95 |
|
| 96 |
print(f"Response 2: {response2} Score: {score:.3f}")
|
| 97 |
```
|
|
|
|
| 79 |
tokens.to(device)
|
| 80 |
|
| 81 |
# Score the response
|
| 82 |
+
score = rewardModel(**tokens, alpha=10).item()
|
| 83 |
|
| 84 |
print(f"Question: {question} \n")
|
| 85 |
print(f"Response 1: {response1} Score: {score:.3f}")
|
|
|
|
| 91 |
|
| 92 |
tokens.to(device)
|
| 93 |
|
| 94 |
+
score = rewardModel(**tokens, alpha=10).item()
|
| 95 |
|
| 96 |
print(f"Response 2: {response2} Score: {score:.3f}")
|
| 97 |
```
|