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README.md

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----
-language: en
-tags:
-- instruction-tuned
-license: apache-2.0
-metrics:
-- bleu
-- accuracy
----
-
-# flan-t5-large
-
-## Model Description
-This model is a fine-tuned version of `google/flan-t5-large` on a custom instruction-based dataset. It is designed to perform well on various instruction-following tasks. FLAN-T5 is known for its strong performance in tasks such as text generation, translation, summarization, and more.
-
-## Intended Use
-The model is designed to perform well on instruction-following tasks. It can be used for a variety of natural language processing applications, including but not limited to:
-- Text generation
-- Translation
-- Summarization
-- Question answering
-- Dialogue systems
-
-## Training Data
-The model was fine-tuned using publicly available instruction-based datasets. The data includes a mix of general-purpose and task-specific instructions, ensuring the model's robustness across different types of instructions.
-
-### Dataset Examples
-- Example 1: "Translate the following sentence to French: 'Hello, how are you?'"
-- Example 2: "Summarize the following article: 'The quick brown fox jumps over the lazy dog...'"
-
-## Evaluation Results
-The model was evaluated using standard benchmarks and showed strong performance across a variety of tasks. The evaluation metrics include accuracy, BLEU score for translation tasks, and ROUGE score for summarization tasks.
-
-### Evaluation Metrics
-- Text generation: Perplexity, BLEU
-- Translation: BLEU
-- Summarization: ROUGE
-- Question answering: F1, EM (Exact Match)
-
-## Usage
-To use this model, you can load it with the following code:
-
-```python
-from transformers import AutoModel, AutoTokenizer
-
-model_name = "sh1gechan/flan-t5-large"
-model = AutoModel.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# Example usage
-inputs = tokenizer("Translate the following sentence to French: 'Hello, how are you?'", return_tensors="pt")
-outputs = model.generate(**inputs)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
-
-## Limitations
-While the model performs well on a variety of tasks, it has some limitations:
-- It may not perform as well on highly specialized or domain-specific tasks without further fine-tuning.
-- The model's responses are only as good as the quality and diversity of the training data.
-
-## License
-This model is licensed under the Apache License 2.0.