Update README.md

README.md

@@ -1,24 +1,24 @@
----
-license: mit
-datasets: pt-sk/toxic_classification
-tags:
-- PPO
-- RLHF
-pipeline_tag: text-generation
----
-Aligning the model using Proximal Policy Optimization (PPO). The goal is to train the model to generate non-toxic reviews. The training process utilizes the `trl` library for reinforcement learning, the `transformers` library for model handling, and `datasets` for dataset management.
-Implementation code is available here: [GitHub](https://github.com/sathishkumar67/GPT-2-Non-Toxic-RLHF)
-```python
-# Load model and tokenizer directly
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("pt-sk/GPT2_NonToxic")
-model = AutoModelForCausalLM.from_pretrained("pt-sk/GPT2_NonToxic")
-
-# Example usage
-input_text = "The movie was fantastic"
-inputs = tokenizer(input_text, return_tensors='pt')
-outputs = model.generate(**inputs)
-
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+---
+license: mit
+datasets: Kwaai/toxic_classification
+tags:
+- PPO
+- RLHF
+pipeline_tag: text-generation
+---
+Aligning the model using Proximal Policy Optimization (PPO). The goal is to train the model to generate non-toxic reviews. The training process utilizes the `trl` library for reinforcement learning, the `transformers` library for model handling, and `datasets` for dataset management.
+Implementation code is available here: [GitHub](https://github.com/sathishkumar67/GPT-2-Non-Toxic-RLHF)
+```python
+# Load model and tokenizer directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Kwaai/GPT2_NonToxic")
+model = AutoModelForCausalLM.from_pretrained("Kwaai/GPT2_NonToxic")
+
+# Example usage
+input_text = "The movie was fantastic"
+inputs = tokenizer(input_text, return_tensors='pt')
+outputs = model.generate(**inputs)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```