Instructions to use dot-ammar/dotless_model-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dot-ammar/dotless_model-small with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dot-ammar/dotless_model-small") model = AutoModelForSeq2SeqLM.from_pretrained("dot-ammar/dotless_model-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
base_model: google/t5-v1_1-small
tags:
- generated_from_keras_callback
model-index:
- name: dot-ammar/dotless_model-small
results: []
datasets:
- dot-ammar/AR-dotless-mediumPlus
language:
- ar
metrics:
- accuracy
dot-ammar/dotless_model-small
This model is a fine-tuned version of google/t5-v1_1-small on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 2.0097
- Validation Loss: 1.2814
- Epoch: 1
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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 4.4462 | 2.1080 | 0 |
| 2.0097 | 1.2814 | 1 |
Framework versions
- Transformers 4.33.0
- TensorFlow 2.12.0
- Datasets 2.1.0
- Tokenizers 0.13.3