gokulsrinivasagan / bert_base_train_qnli

huggingface.co
Total runs: 24
24-hour runs: 0
7-day runs: 11
30-day runs: 20
Model's Last Updated: January 28 2025
text-classification

Introduction of bert_base_train_qnli

Model Details of bert_base_train_qnli

bert_base_train_qnli

This model is a fine-tuned version of gokulsrinivasagan/bert_base_train on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5089
  • Accuracy: 0.7498
Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure
Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 256
  • eval_batch_size: 256
  • seed: 10
  • optimizer: Use 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: 50
Training results
Training Loss Epoch Step Validation Loss Accuracy
0.6419 1.0 410 0.6027 0.6685
0.5265 2.0 820 0.5089 0.7498
0.3861 3.0 1230 0.5229 0.7560
0.2714 4.0 1640 0.6005 0.7542
0.1859 5.0 2050 0.7293 0.7454
0.1307 6.0 2460 0.8065 0.7424
0.0989 7.0 2870 0.9293 0.7494
Framework versions
  • Transformers 4.46.3
  • Pytorch 2.2.1+cu118
  • Datasets 2.17.0
  • Tokenizers 0.20.3

Runs of gokulsrinivasagan bert_base_train_qnli on huggingface.co

24
Total runs
0
24-hour runs
11
3-day runs
11
7-day runs
20
30-day runs

More Information About bert_base_train_qnli huggingface.co Model

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bert_base_train_qnli huggingface.co is an AI model on huggingface.co that provides bert_base_train_qnli's model effect (), which can be used instantly with this gokulsrinivasagan bert_base_train_qnli model. huggingface.co supports a free trial of the bert_base_train_qnli model, and also provides paid use of the bert_base_train_qnli. Support call bert_base_train_qnli model through api, including Node.js, Python, http.

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bert_base_train_qnli huggingface.co is an online trial and call api platform, which integrates bert_base_train_qnli's modeling effects, including api services, and provides a free online trial of bert_base_train_qnli, you can try bert_base_train_qnli online for free by clicking the link below.

gokulsrinivasagan bert_base_train_qnli online free url in huggingface.co:

https://huggingface.co/gokulsrinivasagan/bert_base_train_qnli

bert_base_train_qnli install

bert_base_train_qnli is an open source model from GitHub that offers a free installation service, and any user can find bert_base_train_qnli on GitHub to install. At the same time, huggingface.co provides the effect of bert_base_train_qnli install, users can directly use bert_base_train_qnli installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

bert_base_train_qnli install url in huggingface.co:

https://huggingface.co/gokulsrinivasagan/bert_base_train_qnli

Url of bert_base_train_qnli

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gokulsrinivasagan
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