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