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_editorials_01_03_2022-15_20_12
results: []
distilBERT_token_itr0_0.0001_editorials_01_03_2022-15_20_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: 0.1290
- Precision: 0.0637
- Recall: 0.0080
- F1: 0.0141
- Accuracy: 0.9707
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 | 15 | 0.0733 | 0.04 | 0.0055 | 0.0097 | 0.9861 |
| No log | 2.0 | 30 | 0.0732 | 0.04 | 0.0055 | 0.0097 | 0.9861 |
| No log | 3.0 | 45 | 0.0731 | 0.04 | 0.0055 | 0.0097 | 0.9861 |
| No log | 4.0 | 60 | 0.0716 | 0.04 | 0.0055 | 0.0097 | 0.9861 |
| No log | 5.0 | 75 | 0.0635 | 0.04 | 0.0055 | 0.0097 | 0.9861 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3