distilBERT_token_itr0_0.0001_all_01_03_2022-15_22_12
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2811
- Precision: 0.3231
- Recall: 0.5151
- F1: 0.3971
- Accuracy: 0.8913
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 30 | 0.2881 | 0.2089 | 0.3621 | 0.2650 | 0.8715 |
No log | 2.0 | 60 | 0.2500 | 0.2619 | 0.3842 | 0.3115 | 0.8845 |
No log | 3.0 | 90 | 0.2571 | 0.2327 | 0.4338 | 0.3030 | 0.8809 |
No log | 4.0 | 120 | 0.2479 | 0.3051 | 0.4761 | 0.3719 | 0.8949 |
No log | 5.0 | 150 | 0.2783 | 0.3287 | 0.4761 | 0.3889 | 0.8936 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
- Downloads last month
- 2
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.