Update README.md

README.md

@@ -2,13 +2,14 @@
 library_name: transformers
 license: mit
 datasets:
-- ajaykarthick/imdb-movie-reviews # Assuming this is the dataset you used on HF
+- ajaykarthick/imdb-movie-reviews
 language:
 - en
 metrics:
 - accuracy
 - f1
 - recall
+- precision
 base_model:
 - distilbert/distilbert-base-uncased-finetuned-sst-2-english
 ---
@@ -77,22 +78,25 @@ from transformers import AutoModelForSequenceClassification, AutoTokenizer
 import torch
 
 # Load the model and tokenizer from the Hugging Face Hub
-model_name = "DeepAxion/distilbert-imdb-sentiment" # REPLACE with your actual model ID
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+model_name = "DeepAxion/distilbert-imdb-sentiment" 
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+# put the model in eval mode
+model.eval()
 
 # Example Inference
 text = "This movie totally blew me away, absolutely brilliant acting and a fantastic plot!"
 
 inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
 
+# turn on eval mode
 with torch.no_grad():
     outputs = model(**inputs)
     logits = outputs.logits
     probabilities = torch.softmax(logits, dim=-1)
     prediction = torch.argmax(probabilities, dim=-1).item()
 
-sentiment_labels = {0: "Negative", 1: "Positive"} # Assuming 0: Negative, 1: Positive
+sentiment_labels = {0: "Negative", 1: "Positive"}
 
 print(f"Input Text: \"{text}\"")
 print(f"Predicted Sentiment: {sentiment_labels[prediction]}")
@@ -107,10 +111,10 @@ The model was fine-tuned on the IMDb Large Movie Review Dataset. This dataset co
 
 Dataset Card: https://huggingface.co/datasets/ajaykarthick/imdb-movie-reviews (or the official IMDb dataset link if different)
 
-## Preprocessing
+### Preprocessing
 Text was tokenized using the DistilBertTokenizerFast associated with the base model. Input sequences were truncated to a maximum length of 512 tokens and padded to the longest sequence in the batch. Labels were mapped to 0 for negative and 1 for positive.
 
-## Training Hyperparameters
+### Training Hyperparameters
 - Training regime: Mixed precision (fp16) was likely used for faster training and reduced memory footprint. (Confirm this if you know your specific training setup)
 
 - Optimizer: AdamW
@@ -125,7 +129,24 @@ Text was tokenized using the DistilBertTokenizerFast associated with the base mo
 
 - Framework: PyTorch
 
-## Speeds, Sizes, Times
+### Speeds, Sizes, Times
 Training Time: [E.g., Approximately 1-2 hours on a single Colab T4 GPU] (Estimate based on your experience)
 
-Model Size: The model.safetensors file is approximately 255 MB.
+Model Size: The model.safetensors file is approximately 255 MB.
+
+## Metrics
+The primary evaluation metrics used were:
+
+- Accuracy: The proportion of correctly classified samples.
+- F1-Score (weighted/macro): A measure combining precision and recall, useful for balanced assessment.
+- Recall: The proportion of actual positive/negative samples that were correctly identified.
+- Precision: The proportion of classified postive/negative that were actually positive/negative
+
+### Result
+- Accuracy: 94%
+- Recall: 94%
+- Precision: 94%
+- F1: 93%
+
+## Summary
+The fine-tuned DistilBERT model demonstrates strong performance on the IMDb sentiment classification task, achieving high accuracy, F1-score, and recall on the test set.