<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
distilbert-base-uncased-finetuned-mic
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5640
- Accuracy: 0.7809
- F1: 0.8769
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: 2.740146306575944e-05
- train_batch_size: 400
- eval_batch_size: 400
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 18 | 0.7080 | 0.7232 | 0.8394 |
| No log | 2.0 | 36 | 0.4768 | 0.8443 | 0.9156 |
| No log | 3.0 | 54 | 0.5714 | 0.7866 | 0.8806 |
| No log | 4.0 | 72 | 0.7035 | 0.7151 | 0.8339 |
| No log | 5.0 | 90 | 0.5640 | 0.7809 | 0.8769 |
Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.1
- Datasets 2.0.0
- Tokenizers 0.11.0
