Create README.md

README.md

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+---
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: text-classification
+library_name: transformers
+---
+
+# PLEASE CHECK ED FOR TOKEN
+
+
+
+
+
+
+# Model Evaluation Guide
+
+This document provides the necessary instructions to evaluate a pre-trained sequence classification model using a test dataset.
+
+## Prerequisites
+
+Before running the evaluation pipeline, ensure you have the following installed:
+
+- Python 3.7+
+- Required Python libraries  
+  Install them by running:
+
+```bash
+pip install transformers datasets evaluate torch
+```
+
+## Dataset Information
+
+The test dataset is hosted on the Hugging Face Hub under the namespace `CIS5190ml/Dataset`. The dataset should have the following structure:
+- Column: `title`
+- Column: `label`
+
+Example entries:
+- "Jack Carr's take on the late Tom Clancy..." (label: 0)
+- "Feeding America CEO asks community to help..." (label: 0)
+- "Trump's campaign rival decides between..." (label: 0)
+
+## Model Information
+
+The model being evaluated is hosted under the Hugging Face Hub namespace `CIS5190ml/bert3`.
+
+## Evaluation Pipeline
+
+The complete evaluation pipeline is provided in the file:
+**Evaluation_Pipeline.ipynb**
+
+This Jupyter Notebook walks you through the following steps:
+1. Loading the pre-trained model and tokenizer
+2. Loading and preprocessing the test dataset
+3. Running predictions on the test data
+4. Computing the evaluation metric (e.g., accuracy)
+
+## Quick Start
+
+Clone this repository and navigate to the directory:
+
+```bash
+git clone <repository-url>
+cd <repository-directory>
+```
+
+Open the Jupyter Notebook:
+
+```bash
+jupyter notebook Evaluation_Pipeline.ipynb
+```
+
+Follow the step-by-step instructions in the notebook to evaluate the model.
+
+## Code Example
+
+Here is an overview of the evaluation pipeline used in the notebook:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from datasets import load_dataset
+import evaluate
+import torch
+from torch.utils.data import DataLoader
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("CIS5190ml/bert3")
+model = AutoModelForSequenceClassification.from_pretrained("CIS5190ml/bert3")
+
+# Load dataset
+ds = load_dataset("CIS5190ml/test_20_rows", split="train")
+
+# Preprocessing
+def preprocess_function(examples):
+    return tokenizer(examples["title"], truncation=True, padding="max_length")
+
+encoded_ds = ds.map(preprocess_function, batched=True)
+encoded_ds = encoded_ds.remove_columns([col for col in encoded_ds.column_names if col not in ["input_ids", "attention_mask", "label"]])
+encoded_ds.set_format("torch")
+
+# Create DataLoader
+test_loader = DataLoader(encoded_ds, batch_size=8)
+
+# Evaluate
+accuracy = evaluate.load("accuracy")
+model.eval()
+
+for batch in test_loader:
+    with torch.no_grad():
+        outputs = model(input_ids=batch["input_ids"], attention_mask=batch["attention_mask"])
+        preds = torch.argmax(outputs.logits, dim=-1)
+        accuracy.add_batch(predictions=preds, references=batch["label"])
+
+final_accuracy = accuracy.compute()
+print("Accuracy:", final_accuracy["accuracy"])
+```
+
+## Output
+
+After running the pipeline, the evaluation metric (e.g., accuracy) will be displayed in the notebook output. Example:
+
+```
+Accuracy: 0.85
+```
+
+## Notes
+
+* If your dataset or column names differ, update the relevant sections in the notebook.
+* To use a different evaluation metric, modify the `evaluate.load()` function in the notebook.
+* For any issues or questions, please feel free to reach out.