Update README.md

README.md

@@ -3,35 +3,60 @@ library_name: transformers
 license: apache-2.0
 base_model: google-bert/bert-base-cased
 tags:
+- sentiment-analysis
+- text-classification
+- imdb
 - generated_from_trainer
 metrics:
 - accuracy
 model-index:
-- name: results
-  results: []
+- name: BERT IMDB Sentiment Classifier
+  results:
+  - task:
+      type: text-classification
+      name: Sentiment Analysis
+    dataset:
+      name: IMDB
+      type: stanfordnlp/imdb
+    metrics:
+    - type: accuracy
+      value: 0.928
+      name: Test Accuracy
+  
+datasets:
+- stanfordnlp/imdb
 ---
+# BERT Fine-tuned on IMDB Reviews
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+Fine-tuned BERT-base-cased for binary sentiment classification on movie reviews.
 
-# results
+This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on [Stanford IMDB dataset](https://huggingface.co/datasets/stanfordnlp/imdb).
 
-This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on an unknown dataset.
-It achieves the following results on the evaluation set:
+**Test Results:**
+- Accuracy: 92.8%
 - Loss: 0.2904
-- Accuracy: 0.928
 
 ## Model description
 
-More information needed
+This model classifies movie reviews as positive or negative sentiment. Fine-tuned from `google-bert/bert-base-cased` on the IMDB dataset using HuggingFace Trainer.
 
 ## Intended uses & limitations
 
-More information needed
+**Uses:**
+- Sentiment analysis on movie reviews
+- General sentiment classification on similar review-style text
+
+**Limitations:**
+- Trained specifically on movie reviews - may not generalize well to other domains
+- Binary classification only (positive/negative)
+- Maximum sequence length: 512 tokens
 
 ## Training and evaluation data
 
-More information needed
+- **Dataset:** [Stanford IMDB](https://huggingface.co/datasets/stanfordnlp/imdb)
+- **Size:** 50,000 reviews total
+- **Split:** 20,000 train / 5,000 validation / 25,000 test
+- **Classes:** Binary (0=Negative, 1=Positive), perfectly balanced
 
 ## Training procedure
 
@@ -60,4 +85,4 @@ The following hyperparameters were used during training:
 - Transformers 4.57.3
 - Pytorch 2.9.1+cu128
 - Datasets 4.4.1
-- Tokenizers 0.22.1
+- Tokenizers 0.22.1