sentence_eval1 / README.md
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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
model-index:
  - name: sentence_eval1
    results: []

sentence_eval1

This model is a fine-tuned version of roberta-large-mnli on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4766
  • Precision: {'precision': 0.863681451041519}
  • Recall: {'recall': 0.8702170188463735}
  • F1: {'f1': 0.8669369177156675}
  • Acc: {'accuracy': 0.8073120494335736}

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: 48
  • eval_batch_size: 32
  • 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 Validation Loss Precision Recall F1 Acc
0.437 1.0 1771 0.4753 {'precision': 0.9119431443924424} {'recall': 0.7511422044545973} {'f1': 0.8237688874970641} {'accuracy': 0.7681771369721936}
0.367 2.0 3542 0.4342 {'precision': 0.8658256880733946} {'recall': 0.8623643632210166} {'f1': 0.8640915593705294} {'accuracy': 0.8043254376930999}
0.2915 3.0 5313 0.4766 {'precision': 0.863681451041519} {'recall': 0.8702170188463735} {'f1': 0.8669369177156675} {'accuracy': 0.8073120494335736}

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.0+cu116
  • Datasets 2.7.1
  • Tokenizers 0.13.2