metadata
library_name: transformers
license: mit
base_model: deepset/gbert-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gbert-large-topic_classification
results: []
gbert-large-topic_classification
This model is a fine-tuned version of deepset/gbert-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5093
- Precision: 0.9100
- Recall: 0.8993
- F1: 0.9042
- Accuracy: 0.9167
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- 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 | 44 | 0.6465 | 0.8897 | 0.8138 | 0.8179 | 0.8480 |
| No log | 2.0 | 88 | 0.2949 | 0.9116 | 0.9110 | 0.9106 | 0.9118 |
| No log | 3.0 | 132 | 0.4110 | 0.9298 | 0.8939 | 0.9020 | 0.9167 |
| No log | 4.0 | 176 | 0.6242 | 0.9261 | 0.8756 | 0.8911 | 0.9020 |
| No log | 5.0 | 220 | 0.5606 | 0.9208 | 0.8757 | 0.8897 | 0.9020 |
| No log | 6.0 | 264 | 0.6164 | 0.9201 | 0.8867 | 0.9001 | 0.9069 |
| No log | 7.0 | 308 | 0.4898 | 0.9155 | 0.9001 | 0.9071 | 0.9167 |
| No log | 8.0 | 352 | 0.4999 | 0.9191 | 0.9029 | 0.9102 | 0.9216 |
| No log | 9.0 | 396 | 0.5073 | 0.9100 | 0.8993 | 0.9042 | 0.9167 |
| No log | 10.0 | 440 | 0.5093 | 0.9100 | 0.8993 | 0.9042 | 0.9167 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1