Create README.md

README.md

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+---
+language: en
+thumbnail: https://example.com/thumbnail.png
+tags:
+- paraphrasing
+- T5
+- text generation
+- NLP
+- transformers
+license: mit
+datasets:
+- mteb/quora
+metrics:
+- accuracy
+base_model:
+- humarin/chatgpt_paraphraser_on_T5_base
+library_name: transformers
+---
+
+# ChatGPT and T5 Base Paraphraser
+
+This model is a fine-tuned version of the T5 transformer model designed for paraphrasing questions using the ChatGPT architecture.
+
+## Model Description
+
+The `chat_gpt_and_t5_base_paraphraser` model is trained to generate paraphrased versions of input questions by utilizing a sequence-to-sequence approach. The model leverages the T5 architecture and has been fine-tuned on the Quora Question-Answer dataset to improve its ability to create diverse and meaningful paraphrases.
+
+## Intended Use
+
+This model is intended for applications where paraphrasing of text is required, such as:
+
+- Chatbots
+- Question-answering systems
+- Content generation
+- Educational tools
+
+## How to Use
+
+To use the model, install the Hugging Face `transformers` library and follow these steps:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the model and tokenizer
+model_name = "jaesani/chat_gpt_and_t5_base_paraphraser"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+def paraphrase(question, max_length=128):
+    input_ids = tokenizer(f'paraphrase: {question}', return_tensors="pt", padding="longest", max_length=max_length, truncation=True).input_ids
+    outputs = model.generate(input_ids, max_length=max_length)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Example usage
+paraphrased_text = paraphrase("What are the best places to see in New York?")
+print(paraphrased_text)
+
+
+## Training Data
+   The model was fine-tuned using the Quora Question-Answer Dataset, which consists of pairs of questions that may or may not be paraphrases of each other.
+
+## Evaluation
+   The model's performance can be evaluated based on the diversity and coherence of the paraphrases it generates. Specific metrics can include BLEU scores and human evaluations for semantic similarity.
+
+## Limitations
+  The model may produce paraphrases that are not contextually relevant.
+    It may struggle with highly technical or domain-specific language.
+    Generated paraphrases might be similar for closely related input questions.
+
+## License
+  This model is licensed under MIT License.