metadata
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: sentence-compression
results: []
sentence-compression
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3054
- Accuracy: 0.8802
- F1: 0.7439
- Precision: 0.7661
- Recall: 0.7229
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: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.317 | 1.0 | 200 | 0.3566 | 0.8513 | 0.6227 | 0.7998 | 0.5099 |
| 0.346 | 2.0 | 400 | 0.5853 | 0.7919 | 0.6724 | 0.5413 | 0.8875 |
| 0.2365 | 3.0 | 600 | 0.3054 | 0.8802 | 0.7439 | 0.7661 | 0.7229 |
Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3