File size: 3,356 Bytes
10b48ae 96aadb6 10b48ae 1b2cf09 96aadb6 10b48ae |
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 |
---
base_model: dccuchile/bert-base-spanish-wwm-uncased
tags:
- generated_from_keras_callback
model-index:
- name: lulygavri/sub1-trans
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. -->
# lulygavri/sub1-trans
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0428
- Validation Loss: 0.4923
- Train Accuracy: 0.9
- Train Precision: [0.90909091 0. ]
- Train Precision W: 0.8273
- Train Recall: [0.98901099 0. ]
- Train Recall W: 0.9
- Train F1: [0.94736842 0. ]
- Train F1 W: 0.8621
- Epoch: 5
## 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': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 34, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 500, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Train Precision | Train Precision W | Train Recall | Train Recall W | Train F1 | Train F1 W | Epoch |
|:----------:|:---------------:|:--------------:|:-----------------------:|:-----------------:|:-----------------------:|:--------------:|:-----------------------:|:----------:|:-----:|
| 0.6479 | 0.4942 | 0.91 | [0.91 0. ] | 0.8281 | [1. 0.] | 0.91 | [0.95287958 0. ] | 0.8671 | 1 |
| 0.4492 | 0.4305 | 0.8 | [0.9382716 0.21052632] | 0.8728 | [0.83516484 0.44444444] | 0.8 | [0.88372093 0.28571429] | 0.8299 | 2 |
| 0.1358 | 0.3080 | 0.88 | [0.90721649 0. ] | 0.8256 | [0.96703297 0. ] | 0.88 | [0.93617021 0. ] | 0.8519 | 3 |
| 0.0545 | 0.3801 | 0.9 | [0.90909091 0. ] | 0.8273 | [0.98901099 0. ] | 0.9 | [0.94736842 0. ] | 0.8621 | 4 |
| 0.0428 | 0.4923 | 0.9 | [0.90909091 0. ] | 0.8273 | [0.98901099 0. ] | 0.9 | [0.94736842 0. ] | 0.8621 | 5 |
### Framework versions
- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
|