metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
base_model: bert-base-uncased
model-index:
- name: distilBERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_16_57
results: []
distilBERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_16_57
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: 0.5923
- Precision: 0.0039
- Recall: 0.0212
- F1: 0.0066
- Accuracy: 0.7084
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 | 10 | 0.6673 | 0.0476 | 0.0128 | 0.0202 | 0.6652 |
| No log | 2.0 | 20 | 0.6211 | 0.0 | 0.0 | 0.0 | 0.6707 |
| No log | 3.0 | 30 | 0.6880 | 0.0038 | 0.0128 | 0.0058 | 0.6703 |
| No log | 4.0 | 40 | 0.6566 | 0.0030 | 0.0128 | 0.0049 | 0.6690 |
| No log | 5.0 | 50 | 0.6036 | 0.0 | 0.0 | 0.0 | 0.6868 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3