metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: bert-base-cased-finetuned-qnli
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE QNLI
          type: glue
          args: qnli
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9099395936298736

bert-base-cased-finetuned-qnli

This model is a fine-tuned version of bert-base-cased on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.3986
Accuracy: 0.9099

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

This model is trained using the run_glue script. The following command was used:

#!/usr/bin/bash

python ../run_glue.py \
  --model_name_or_path bert-base-cased \
  --task_name qnli \
  --do_train \
  --do_eval \
  --max_seq_length 512 \
  --per_device_train_batch_size 16 \
  --learning_rate 2e-5 \
  --num_train_epochs 3 \
  --output_dir bert-base-cased-finetuned-qnli \
  --push_to_hub \
  --hub_strategy all_checkpoints \
  --logging_strategy epoch \
  --save_strategy epoch \
  --evaluation_strategy epoch \

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss
0.337	1.0	6547	0.9013	0.2448
0.1971	2.0	13094	0.9143	0.2839
0.1175	3.0	19641	0.9099	0.3986

Framework versions

Transformers 4.11.0.dev0
Pytorch 1.9.0
Datasets 1.12.1
Tokenizers 0.10.3