albert-large-v2_ner_wnut_17
This model is a fine-tuned version of albert-large-v2 on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2429
- Precision: 0.7446
- Recall: 0.5335
- F1: 0.6216
- Accuracy: 0.9582
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: cosine
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.3051 | 0.7929 | 0.3206 | 0.4566 | 0.9410 |
| No log | 2.0 | 426 | 0.2151 | 0.7443 | 0.4665 | 0.5735 | 0.9516 |
| 0.17 | 3.0 | 639 | 0.2310 | 0.7364 | 0.5012 | 0.5964 | 0.9559 |
| 0.17 | 4.0 | 852 | 0.2387 | 0.7564 | 0.5311 | 0.6240 | 0.9578 |
| 0.0587 | 5.0 | 1065 | 0.2429 | 0.7446 | 0.5335 | 0.6216 | 0.9582 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1