---
tags:
- generated_from_keras_callback
model-index:
- name: ASL_t5_movinet
  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. -->

# ASL_t5_movinet

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5272
- Train Top 1: 0.8966
- Train Top 5: 0.9383
- Validation Loss: 0.7459
- Validation Top 1: 0.8710
- Validation Top 5: 0.9142
- Train Bleu: 0.0
- Train Gen Len: 2.0
- 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': 'Adafactor', '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': 1e-04, 'beta_2_decay': -0.8, 'epsilon_1': 1e-30, 'epsilon_2': 0.001, 'clip_threshold': 1.0, 'relative_step': True}
- training_precision: float32

### Training results

| Train Loss | Train Top 1 | Train Top 5 | Validation Loss | Validation Top 1 | Validation Top 5 | Train Bleu | Train Gen Len | Epoch |
|:----------:|:-----------:|:-----------:|:---------------:|:----------------:|:----------------:|:----------:|:-------------:|:-----:|
| 0.5971     | 0.8879      | 0.9305      | 0.7228          | 0.8744           | 0.9170           | 0.0        | 2.0           | 0     |
| 0.5567     | 0.8928      | 0.9353      | 0.7311          | 0.8736           | 0.9156           | 0.0        | 2.0           | 1     |
| 0.5272     | 0.8966      | 0.9383      | 0.7459          | 0.8710           | 0.9142           | 0.0        | 2.0           | 2     |


### Framework versions

- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.15.0
- Tokenizers 0.14.1