File size: 4,339 Bytes
6a67ed6 5a30771 6a67ed6 f737b47 a5bd39d 705dec9 cf8ca10 59e0ce7 5dab9e2 ca3bef7 6a7bb28 34a0ab6 faa756a ef4747a 5a30771 6a67ed6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: edyfjm07/distilbert-base-uncased-QA4-finetuned-squad-es
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. -->
# edyfjm07/distilbert-base-uncased-QA4-finetuned-squad-es
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: 0.3831
- Train End Logits Accuracy: 0.8456
- Train Start Logits Accuracy: 0.8834
- Validation Loss: 1.1614
- Validation End Logits Accuracy: 0.7429
- Validation Start Logits Accuracy: 0.7931
- Epoch: 12
## 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: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 5474, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 3.8949 | 0.1733 | 0.1891 | 2.4981 | 0.3918 | 0.3981 | 0 |
| 2.0479 | 0.4097 | 0.4811 | 1.6575 | 0.4890 | 0.6113 | 1 |
| 1.4343 | 0.5599 | 0.6166 | 1.3371 | 0.5768 | 0.6426 | 2 |
| 1.0892 | 0.6313 | 0.6891 | 1.1850 | 0.6677 | 0.6865 | 3 |
| 0.9172 | 0.6870 | 0.7405 | 1.1305 | 0.6771 | 0.7335 | 4 |
| 0.7470 | 0.7258 | 0.7910 | 1.0674 | 0.7147 | 0.7524 | 5 |
| 0.6728 | 0.7426 | 0.8088 | 1.0843 | 0.7116 | 0.7680 | 6 |
| 0.5989 | 0.7721 | 0.8403 | 1.0787 | 0.7304 | 0.7649 | 7 |
| 0.4988 | 0.8057 | 0.8582 | 1.1091 | 0.7398 | 0.7618 | 8 |
| 0.4674 | 0.8214 | 0.8540 | 1.1150 | 0.7367 | 0.7774 | 9 |
| 0.4173 | 0.8256 | 0.8782 | 1.1434 | 0.7335 | 0.7774 | 10 |
| 0.3804 | 0.8319 | 0.8897 | 1.1256 | 0.7335 | 0.7900 | 11 |
| 0.3831 | 0.8456 | 0.8834 | 1.1614 | 0.7429 | 0.7931 | 12 |
### Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1
|