|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_keras_callback |
|
model-index: |
|
- name: silviacamplani/distilbert-uncase-direct-finetuning-ai-ner |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should |
|
probably proofread and complete it, then remove this comment. --> |
|
|
|
# silviacamplani/distilbert-uncase-direct-finetuning-ai-ner |
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Train Loss: 1.6021 |
|
- Validation Loss: 1.6163 |
|
- Epoch: 9 |
|
|
|
## 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: |
|
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 60, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} |
|
- training_precision: mixed_float16 |
|
|
|
### Training results |
|
|
|
| Train Loss | Validation Loss | Epoch | |
|
|:----------:|:---------------:|:-----:| |
|
| 3.2752 | 3.0320 | 0 | |
|
| 2.7791 | 2.5293 | 1 | |
|
| 2.2674 | 2.0340 | 2 | |
|
| 1.8952 | 1.8222 | 3 | |
|
| 1.7933 | 1.7669 | 4 | |
|
| 1.7352 | 1.7158 | 5 | |
|
| 1.6868 | 1.6706 | 6 | |
|
| 1.6242 | 1.6412 | 7 | |
|
| 1.5899 | 1.6234 | 8 | |
|
| 1.6021 | 1.6163 | 9 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.20.1 |
|
- TensorFlow 2.6.4 |
|
- Datasets 2.1.0 |
|
- Tokenizers 0.12.1 |
|
|
