Update README.md

README.md

@@ -1,93 +1,105 @@
-# BART-Base XSum Summarization Model
-
-## Model Description
-
-The model is a sequence-to-sequence transformer based on the BART architecture. It was fine-tuned on the [XSum](https://huggingface.co/datasets/EdinburghNLP/xsum) dataset using the `facebook/bart-base` model, which consists of news articles paired with short summaries.
-
-## Model Training Details
-
-### Training Dataset
-
-- **Dataset:** [XSum](https://huggingface.co/datasets/EdinburghNLP/xsum)
-- **Splits:**
-  - **Train:** 204,045 examples (filtered to 203,966 examples)
-  - **Validation:** 11,332 examples (filtered to 11,326 examples)
-  - **Test:** 11,334 examples (filtered to 11,331 examples)
-- **Preprocessing:**
-  - Tokenization of documents and summaries using the `facebook/bart-base` tokenizer.
-  - Filtering out examples with very short documents or summaries.
-  - Truncating inputs to a maximum length of 1024 tokens for documents and 512 tokens for summaries.
-
-### Training Configuration
-
-The model was fine-tuned using the `Seq2SeqTrainer` from the Hugging Face Transformers library with the following training arguments:
-
-- **Evaluation Strategy:** Evaluation at the end of each epoch
-- **Learning Rate:** 3e-5
-- **Batch Size:**
-  - **Training:** 16 per device
-  - **Evaluation:** 32 per device
-- **Gradient Accumulation Steps:** 1
-- **Weight Decay:** 0.01
-- **Number of Epochs:** 5
-- **Warmup Steps:** 1000
-- **Learning Rate Scheduler:** Cosine scheduler
-- **Label Smoothing Factor:** 0.1
-- **Mixed Precision:** FP16 enabled
-- **Prediction:** Uses `predict_with_generate` to compute summaries during evaluation
-- **Metric for Best Model:** `rougeL`
-
-## Model Results
-
-### Evaluation Metrics
-
-After fine-tuning, the model achieved the following scores:
-
-- **Validation Set:**
-  - **Eval Loss:** 3.0508
-  - **ROUGE-1:** 39.2079
-  - **ROUGE-2:** 17.8686
-  - **ROUGE-L:** 32.4777
-  - **ROUGE-Lsum:** 32.4734
-- **Test Set:**
-  - **Eval Loss:** 3.0607
-  - **ROUGE-1:** 39.2149
-  - **ROUGE-2:** 17.7573
-  - **ROUGE-L:** 32.4190
-  - **ROUGE-Lsum:** 32.4020
-
-### Final Training Loss
-
-- **Final Training Loss:** 2.9226  
-- **Final Validation Loss:** 3.0508
-
-## Model Usage
-
-You can easily use the model for summarization tasks using the Hugging Face `pipeline`. Below is an example:
-
-```python
-from transformers import pipeline
-
-# Load the summarization pipeline using the fine-tuned model
-summarizer = pipeline("summarization", model="Prikshit7766/bart-base-xsum")
-
-# Input text for summarization
-text = (
-    "In a significant breakthrough in renewable energy, scientists have developed "
-    "a novel solar panel technology that promises to dramatically reduce costs and "
-    "increase efficiency. The new panels are lighter, more durable, and easier to install "
-    "than conventional models, marking a major advancement in sustainable energy solutions. "
-    "Experts believe this innovation could lead to wider adoption of solar power across residential "
-    "and commercial sectors, ultimately reducing global reliance on fossil fuels."
-)
-
-# Generate summary
-summary = summarizer(text)[0]["summary_text"]
-print("Generated Summary:", summary)
-```
-
-**Example Output:**
-
-```
-Generated Summary: Scientists at the University of California, Berkeley, have developed a new type of solar panel.
+---
+datasets:
+- EdinburghNLP/xsum
+language:
+- en
+metrics:
+- rouge
+base_model:
+- facebook/bart-base
+pipeline_tag: summarization
+library_name: transformers
+---
+# BART-Base XSum Summarization Model
+
+## Model Description
+
+The model is a sequence-to-sequence transformer based on the BART architecture. It was fine-tuned on the [XSum](https://huggingface.co/datasets/EdinburghNLP/xsum) dataset using the `facebook/bart-base` model, which consists of news articles paired with short summaries.
+
+## Model Training Details
+
+### Training Dataset
+
+- **Dataset:** [XSum](https://huggingface.co/datasets/EdinburghNLP/xsum)
+- **Splits:**
+  - **Train:** 204,045 examples (filtered to 203,966 examples)
+  - **Validation:** 11,332 examples (filtered to 11,326 examples)
+  - **Test:** 11,334 examples (filtered to 11,331 examples)
+- **Preprocessing:**
+  - Tokenization of documents and summaries using the `facebook/bart-base` tokenizer.
+  - Filtering out examples with very short documents or summaries.
+  - Truncating inputs to a maximum length of 1024 tokens for documents and 512 tokens for summaries.
+
+### Training Configuration
+
+The model was fine-tuned using the `Seq2SeqTrainer` from the Hugging Face Transformers library with the following training arguments:
+
+- **Evaluation Strategy:** Evaluation at the end of each epoch
+- **Learning Rate:** 3e-5
+- **Batch Size:**
+  - **Training:** 16 per device
+  - **Evaluation:** 32 per device
+- **Gradient Accumulation Steps:** 1
+- **Weight Decay:** 0.01
+- **Number of Epochs:** 5
+- **Warmup Steps:** 1000
+- **Learning Rate Scheduler:** Cosine scheduler
+- **Label Smoothing Factor:** 0.1
+- **Mixed Precision:** FP16 enabled
+- **Prediction:** Uses `predict_with_generate` to compute summaries during evaluation
+- **Metric for Best Model:** `rougeL`
+
+## Model Results
+
+### Evaluation Metrics
+
+After fine-tuning, the model achieved the following scores:
+
+- **Validation Set:**
+  - **Eval Loss:** 3.0508
+  - **ROUGE-1:** 39.2079
+  - **ROUGE-2:** 17.8686
+  - **ROUGE-L:** 32.4777
+  - **ROUGE-Lsum:** 32.4734
+- **Test Set:**
+  - **Eval Loss:** 3.0607
+  - **ROUGE-1:** 39.2149
+  - **ROUGE-2:** 17.7573
+  - **ROUGE-L:** 32.4190
+  - **ROUGE-Lsum:** 32.4020
+
+### Final Training Loss
+
+- **Final Training Loss:** 2.9226  
+- **Final Validation Loss:** 3.0508
+
+## Model Usage
+
+You can easily use the model for summarization tasks using the Hugging Face `pipeline`. Below is an example:
+
+```python
+from transformers import pipeline
+
+# Load the summarization pipeline using the fine-tuned model
+summarizer = pipeline("summarization", model="Prikshit7766/bart-base-xsum")
+
+# Input text for summarization
+text = (
+    "In a significant breakthrough in renewable energy, scientists have developed "
+    "a novel solar panel technology that promises to dramatically reduce costs and "
+    "increase efficiency. The new panels are lighter, more durable, and easier to install "
+    "than conventional models, marking a major advancement in sustainable energy solutions. "
+    "Experts believe this innovation could lead to wider adoption of solar power across residential "
+    "and commercial sectors, ultimately reducing global reliance on fossil fuels."
+)
+
+# Generate summary
+summary = summarizer(text)[0]["summary_text"]
+print("Generated Summary:", summary)
+```
+
+**Example Output:**
+
+```
+Generated Summary: Scientists at the University of California, Berkeley, have developed a new type of solar panel.
 ```