Update README.md

README.md

@@ -11,4 +11,72 @@ base_model:
 - google-t5/t5-small
 pipeline_tag: summarization
 library_name: transformers
----
+---
+# Model Card for t5_small Summarization Model
+
+## Model Details
+
+- Model Architecture: T5 (Text-to-Text Transfer Transformer)
+- Variant: t5-small
+- Task: Text Summarization
+- Framework: Hugging Face Transformers
+
+## Training Data
+
+- Dataset: CNN/DailyMail
+- Content: News articles and their summaries
+- Size: Approximately 300,000 article-summary pairs
+
+## Training Procedure
+
+- Fine-tuning method: Using Hugging Face Transformers library
+- Hyperparameters:
+  - Learning rate: 5e-5
+  - Batch size: 8
+  - Number of epochs: 3
+- Optimizer: AdamW
+
+## How to Use
+
+1. Install the Hugging Face Transformers library:
+```
+pip install transformers
+```
+
+2. Load the model:
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("t5-small")
+model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
+```
+
+3. Generate a summary:
+```python
+input_text = "Your input text here"
+inputs = tokenizer("summarize: " + input_text, return_tensors="pt", max_length=512, truncation=True)
+summary_ids = model.generate(inputs["input_ids"], max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
+summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+```
+
+## Evaluation
+
+- Metric: ROUGE scores (Recall-Oriented Understudy for Gisting Evaluation)
+- Exact scores not available, but typically evaluated on:
+  - ROUGE-1 (unigram overlap)
+  - ROUGE-2 (bigram overlap)
+  - ROUGE-L (longest common subsequence)
+
+## Limitations
+
+- Performance may be lower compared to larger T5 variants
+- Optimized for news article summarization, may not perform as well on other text types
+- Limited to input sequences of 512 tokens
+- Generated summaries may sometimes contain factual inaccuracies
+
+## Ethical Considerations
+
+- May inherit biases present in the CNN/DailyMail dataset
+- Not suitable for summarizing sensitive or critical information without human review
+- Users should be aware of potential biases and inaccuracies in generated summaries
+- Should not be used as a sole source of information for decision-making processes