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---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: DearSola/my-awesome-model_1113
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. -->
# DearSola/my-awesome-model_1113
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1382
- Validation Loss: 0.3086
- Train Accuracy: 0.8771
- Train F1: 0.7370
- Train Precision: 0.7584
- Train Recall: 0.7168
- Epoch: 2
## 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 675, '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 | Validation Loss | Train Accuracy | Train F1 | Train Precision | Train Recall | Epoch |
|:----------:|:---------------:|:--------------:|:--------:|:---------------:|:------------:|:-----:|
| 0.4029 | 0.3051 | 0.8618 | 0.6846 | 0.7579 | 0.6243 | 0 |
| 0.2311 | 0.2948 | 0.8701 | 0.7497 | 0.6983 | 0.8092 | 1 |
| 0.1382 | 0.3086 | 0.8771 | 0.7370 | 0.7584 | 0.7168 | 2 |
### Framework versions
- Transformers 4.41.1
- TensorFlow 2.17.0
- Datasets 2.20.0
- Tokenizers 0.19.1
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