---
library_name: transformers
tags:
- generated_from_trainer
- mnli
- text-classification
- bert
metrics:
- accuracy
- f1
model-index:
  - 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
language:
- en
base_model:
- google-bert/bert-base-cased
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. -->

# soonbob/mnli-finetuned-bert-base-cased

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`.

It classifies a pair of sentences into one of the following classes:
- **entailment**
- **neutral**
- **contradiction**

## 🧠 Intended Use

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


It achieves the following results on the evaluation set:
- Loss: 0.8276
- Accuracy: 0.6368
- F1: 0.6358

## ⚙️ Training Details

- 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 hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3

### 🏋️ Training Logs

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8662        | 1.0   | 2455 | 0.8682          | 0.6033   | 0.5946 |
| 0.7964        | 2.0   | 4910 | 0.8449          | 0.6242   | 0.6242 |
| 0.7323        | 3.0   | 7365 | 0.8673          | 0.6237   | 0.6231 |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1