File size: 2,080 Bytes
328b804 724b96d 328b804 b573d20 328b804 b573d20 403728c ed8278f aacdfa8 8dd1351 b1b7342 dcf8247 cdbb590 1ccdf6b 724b96d 328b804 b1326b5 328b804 |
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 |
---
license: mit
base_model: xlnet-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: svenbl80/xlnet-base-cased-finetuned-mnli
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. -->
# svenbl80/xlnet-base-cased-finetuned-mnli
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co./xlnet-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0221
- Validation Loss: 0.7391
- Train Accuracy: 0.8677
- 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: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 245430, '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}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.4828 | 0.4066 | 0.8426 | 0 |
| 0.3364 | 0.3842 | 0.8598 | 1 |
| 0.2419 | 0.3913 | 0.8672 | 2 |
| 0.1713 | 0.4653 | 0.8624 | 3 |
| 0.1212 | 0.5090 | 0.8625 | 4 |
| 0.0857 | 0.5733 | 0.8684 | 5 |
| 0.0620 | 0.6176 | 0.8635 | 6 |
| 0.0435 | 0.6781 | 0.8670 | 7 |
| 0.0309 | 0.7102 | 0.8668 | 8 |
| 0.0221 | 0.7391 | 0.8677 | 9 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.9.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|