Create README.md

README.md

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+---
+language:
+- en
+---
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+model_path = "reciprocate/rm-beluga-7b-hh-full"
+
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+# for SequenceClassification models padding side should be "right"
+tokenizer.padding_side = "right"
+tokenizer.truncation_side = "left"
+reward_fn = pipeline("text-classification", model=model, tokenizer=tokenizer, truncation=True, batch_size=32, max_length=2048, device=0)
+output = reward_fn(["### User: Complete this sentence: I'm 99 percent sure it was someone being an...\n\n### Assistant:\n I'm 99 percent sure it was someone being an idiot.</s>"])
+scores = [x["score"] for x in output]
+scores
+```
+```
+>>> [0.02713249810039997]
+```
+
+```python
+# optionally normalize with mean, std computed on training data
+scores = (np.array(scores) - 0.6816716283619826) / 0.3198637874065531
+```