s444465/projekt-glebokie
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
master
projekt-glebokie/models/roberta
History
s444501 1c37fe2bd2 roberta new
2023-02-12 21:42:54 +01:00
..
all_results.json roberta new 2023-02-12 21:42:54 +01:00
config.json roberta new 2023-02-12 21:42:54 +01:00
eval_results.json roberta new 2023-02-12 21:42:54 +01:00
merges.txt roberta new 2023-02-12 21:42:54 +01:00
predict_results_None.txt roberta new 2023-02-12 21:42:54 +01:00
pytorch_model.bin roberta new 2023-02-12 21:42:54 +01:00
README.md roberta new 2023-02-12 21:42:54 +01:00
special_tokens_map.json roberta new 2023-02-12 21:42:54 +01:00
tokenizer_config.json roberta new 2023-02-12 21:42:54 +01:00
tokenizer.json roberta new 2023-02-12 21:42:54 +01:00
train_results.json roberta new 2023-02-12 21:42:54 +01:00
trainer_state.json roberta new 2023-02-12 21:42:54 +01:00
training_args.bin roberta new 2023-02-12 21:42:54 +01:00
vocab.json roberta new 2023-02-12 21:42:54 +01:00

README.md

license tags metrics model-index
mit
generated_from_trainer
accuracy
name results
roberta

roberta

This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2195
  • Accuracy: 0.9360

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1.0

Training results

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu116
  • Datasets 2.9.0
  • Tokenizers 0.13.2