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README.md
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- stanfordnlp/sst2
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base_model:
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- openai-community/gpt2
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---
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#
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This model is the **first stage** in a 3-step RLHF (Reinforcement Learning from Human Feedback) pipeline using **GPT-2**. It has been fine-tuned on the **Stanford Sentiment Treebank v2 (SST2)** dataset, focusing on generating sentences with a positive sentiment tone.
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##
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This model is part of the following RLHF project structure:
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##
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Train GPT-2 on sentiment-labeled sentences to mimic human-like, sentiment-aware generation.
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- **Output:** GPT-2 completes it with a positively-toned sentence.
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---
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## ๐ Training Details
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### ๐ง Dataset
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- **Source:** `stanfordnlp/sst2`
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- **Type:** Movie review sentences
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- **Labels:** Positive and Negative
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- **Preprocessing:** Only positive samples retained for SFT
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### โ๏ธ Configuration
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- **Model Base:** `gpt2`
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- **Max Sequence Length:** 128
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- **Batch Size:** 8
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- **Epochs:** 3
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- **Optimizer:** AdamW
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- **Learning Rate:** 5e-5
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- **Precision:** FP16
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---
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## ๐ Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("
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tokenizer = AutoTokenizer.from_pretrained("
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prompt = "The movie was"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=30)
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print(tokenizer.decode(outputs[0]))
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- stanfordnlp/sst2
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base_model:
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- openai-community/gpt2
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pipeline_tag: text-generation
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---
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# GPT-2 SFT Model โ Supervised Fine-Tuning for Positive Sentiment
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This model is the **first stage** in a 3-step RLHF (Reinforcement Learning from Human Feedback) pipeline using **GPT-2**. It has been fine-tuned on the **Stanford Sentiment Treebank v2 (SST2)** dataset, focusing on generating sentences with a positive sentiment tone.
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## Context
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This model is part of the following RLHF project structure:
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## Model Objective
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Train GPT-2 on sentiment-labeled sentences to mimic human-like, sentiment-aware generation.
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- **Output:** GPT-2 completes it with a positively-toned sentence.
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### Dataset
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- **Source:** `stanfordnlp/sst2`
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- **Type:** Movie review sentences
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- **Labels:** Positive and Negative
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- **Preprocessing:** Only positive samples retained for SFT
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("Saif10/sft-model")
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tokenizer = AutoTokenizer.from_pretrained("Saif10/sft-model")
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prompt = "The movie was"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=30)
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print(tokenizer.decode(outputs[0]))
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```
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## Author
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Saif Rathod
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- Hugging Face: Saif10
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- GitHub: Saif-rathod
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