metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: device_recalls_ner_baseline
results: []
device_recalls_ner_baseline
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0010
- Precision: 0.5161
- Recall: 0.5087
- F1: 0.5124
- Accuracy: 0.7492
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 101 | 0.6541 | 0.4257 | 0.3396 | 0.3778 | 0.6959 |
| No log | 2.0 | 202 | 0.5924 | 0.4803 | 0.4754 | 0.4779 | 0.7429 |
| No log | 3.0 | 303 | 0.6068 | 0.4826 | 0.5 | 0.4911 | 0.7283 |
| No log | 4.0 | 404 | 0.6778 | 0.4350 | 0.5173 | 0.4726 | 0.7079 |
| 0.501 | 5.0 | 505 | 0.7109 | 0.4876 | 0.5101 | 0.4986 | 0.7359 |
| 0.501 | 6.0 | 606 | 0.7291 | 0.4929 | 0.5043 | 0.4986 | 0.7448 |
| 0.501 | 7.0 | 707 | 0.8655 | 0.5338 | 0.4798 | 0.5053 | 0.7537 |
| 0.501 | 8.0 | 808 | 0.8715 | 0.5055 | 0.5275 | 0.5163 | 0.7460 |
| 0.501 | 9.0 | 909 | 1.0034 | 0.5027 | 0.5390 | 0.5202 | 0.7467 |
| 0.1555 | 10.0 | 1010 | 1.0010 | 0.5161 | 0.5087 | 0.5124 | 0.7492 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1