This model is a fine-tuned version of GPT-2 on the ParaSCI dataset for paraphrase generation. It takes a sentence as input and generates a paraphrased version of that sentence.

📋 Model Details

Base model: GPT-2 (gpt2)
Task: Paraphrase generation (Causal Language Modeling)
Language: English
Training data: HHousen/ParaSCI
Training steps: 1 epoch on ~270k examples
Precision: fp16 mixed precision
Hardware used: Tesla T4 (Kaggle Notebook GPU)
Framework: 🤗 Transformers, PyTorch
Trained by: [Your Name or HF Username]
License: MIT

💡 Intended Use

✅ Direct Use

Generate paraphrased versions of input English sentences in a general academic/technical writing context.

🚫 Out-of-Scope Use

Not suitable for paraphrasing code, informal language, or other languages (non-English).
Not tested for fairness, bias, or ethical use in downstream applications.

📊 Evaluation

Qualitative Evaluation: Manual checks indicate coherent paraphrased outputs.
Automatic Metrics: Not yet reported.

🛠 Training Details

Dataset: ParaSCI (sentence1 → sentence2)
Preprocessing: Concatenated prompt paraphrase this sentence: {sentence1}\n{sentence2}
Tokenizer: GPT-2 tokenizer with pad_token = eos_token
Batch size: 8
Epochs: 1
Learning rate: 5e-5
Logging and checkpointing: Every 500 steps, using Weights & Biases (wandb)
Max sequence length: 256 tokens

🏁 How to Use

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("your-username/gpt2-parasciparaphrase")
tokenizer = AutoTokenizer.from_pretrained("your-username/gpt2-parasciparaphrase")

input_text = "paraphrase this sentence: AI models can help in automating tasks.\n"
input_ids = tokenizer.encode(input_text, return_tensors="pt")

output = model.generate(input_ids, max_new_tokens=50, do_sample=True, top_k=50, top_p=0.95)
print(tokenizer.decode(output[0], skip_special_tokens=True))

ptkag1712/gpt2-based-on-paraSCI_dataset

Model Card for gpt2-parasciparaphrase

🧠 Model Summary

📋 Model Details

💡 Intended Use

✅ Direct Use

🚫 Out-of-Scope Use

📊 Evaluation

🛠 Training Details

🏁 How to Use

ptkag1712/
gpt2-based-on-paraSCI_dataset

Model Card for `gpt2-parasciparaphrase`