File size: 2,055 Bytes
d0a5ef0 82d27d4 d0a5ef0 62dfb6a 0fae248 2ac2bcb e9fea60 7a0f72c b04357f 040878b 69b805d 82d27d4 d0a5ef0 |
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 |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: svenbl80/roberta-base-finetuned-new-mnli-run-4
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/roberta-base-finetuned-new-mnli-run-4
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0254
- Validation Loss: 0.7597
- Train Accuracy: 0.8592
- 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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.4543 | 0.3920 | 0.8526 | 0 |
| 0.3298 | 0.3979 | 0.8546 | 1 |
| 0.2478 | 0.4089 | 0.8603 | 2 |
| 0.1821 | 0.4577 | 0.8575 | 3 |
| 0.1309 | 0.4901 | 0.8556 | 4 |
| 0.0947 | 0.5514 | 0.8551 | 5 |
| 0.0682 | 0.6368 | 0.8553 | 6 |
| 0.0489 | 0.6589 | 0.8577 | 7 |
| 0.0343 | 0.7216 | 0.8599 | 8 |
| 0.0254 | 0.7597 | 0.8592 | 9 |
### Framework versions
- Transformers 4.28.0
- TensorFlow 2.9.1
- Datasets 2.15.0
- Tokenizers 0.13.3
|