mpnet-adaptation_mitigation-classifier
This model is a fine-tuned version of sentence-transformers/all-mpnet-base-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2117
- Precision Micro: 0.9175
- Precision Weighted: 0.9181
- Precision Samples: 0.9256
- Recall Micro: 0.9281
- Recall Weighted: 0.9281
- Recall Samples: 0.9314
- F1-score: 0.9263
- Accuracy: 0.9082
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: 8e-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
- lr_scheduler_warmup_steps: 200
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision Micro | Precision Weighted | Precision Samples | Recall Micro | Recall Weighted | Recall Samples | F1-score | Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3291 | 1.0 | 1051 | 0.2117 | 0.9175 | 0.9181 | 0.9256 | 0.9281 | 0.9281 | 0.9314 | 0.9263 | 0.9082 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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