File size: 2,028 Bytes
d9cae20 2f87af8 d9cae20 43ca717 c3b9055 24aa08b 2f87af8 d9cae20 |
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 |
---
license: mit
base_model: xlnet-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: dipawidia/xlnet-base-cased-product-review-sentiment-analysis
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. -->
# dipawidia/xlnet-base-cased-product-review-sentiment-analysis
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: 0.1085
- Train Accuracy: 0.9617
- Validation Loss: 0.1910
- Validation Accuracy: 0.9414
- Epoch: 4
## 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': 'AdamW', 'weight_decay': 0.004, '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': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3417 | 0.8491 | 0.1568 | 0.9449 | 0 |
| 0.1943 | 0.9235 | 0.1504 | 0.9466 | 1 |
| 0.1569 | 0.9404 | 0.1612 | 0.9466 | 2 |
| 0.1238 | 0.9572 | 0.1748 | 0.9475 | 3 |
| 0.1085 | 0.9617 | 0.1910 | 0.9414 | 4 |
### Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Tokenizers 0.19.1
|