Update README.md

README.md

@@ -2,12 +2,28 @@
 library_name: transformers
 tags:
 - generated_from_trainer
+- mnli
+- text-classification
+- bert
 metrics:
 - accuracy
 - f1
 model-index:
-- name: hf_transformer
-  results: []
+  - name: mnli-finetuned-bert-base-cased
+    results:
+      - task:
+          type: text-classification
+          name: Natural Language Inference
+        dataset:
+          name: MultiNLI
+          type: nyu-mll/glue
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 0.6368
+          - name: F1
+            type: f1
+            value: 0.6358
 license: mit
 datasets:
 - nyu-mll/glue
@@ -21,29 +37,42 @@ pipeline_tag: text-classification
 <!-- 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. -->
 
-# hf_transformer
+# soonbob/mnli-finetuned-bert-base-cased
 
-This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.8276
-- Accuracy: 0.6368
-- F1: 0.6358
+MNLI 데이터셋을 학습시킨 BERT
+파인튜닝 연습용으로 만든 것입니다.
+
+This is a BERT-based model fine-tuned on the [Multi-Genre Natural Language Inference (MultiNLI)](https://huggingface.co/datasets/glue/viewer/mnli) dataset for the task of **natural language inference** (NLI), using Hugging Face's `Trainer`.
 
-## Model description
+It classifies a pair of sentences into one of the following classes:
+- **entailment**
+- **neutral**
+- **contradiction**
 
-More information needed
+## 🧠 Intended Use
 
-## Intended uses & limitations
+This model can be used for:
+- Evaluating whether one sentence logically follows from another
+- Sentence-pair classification tasks
+- Transfer learning for other NLI-style problems
 
-More information needed
 
-## Training and evaluation data
+It achieves the following results on the evaluation set:
+- Loss: 0.8276
+- Accuracy: 0.6368
+- F1: 0.6358
+
+## ⚙️ Training Details
 
-More information needed
+- Base model: [`bert-base-cased`](https://huggingface.co/bert-base-cased)
+- Dataset: `nyu-mll/glue`, subset: `mnli`
+- Epochs: 3
+- Learning rate: 1e-3
+- Optimizer: AdamW
+- Scheduler: Linear
 
-## Training procedure
 
-### Training hyperparameters
+### 🏋️ Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 0.001
@@ -54,7 +83,7 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 3
 
-### Training results
+### 🏋️ Training Logs
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|