Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use Samuael/ethipic-sec2sec-tigre with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Samuael/ethipic-sec2sec-tigre with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Samuael/ethipic-sec2sec-tigre") model = AutoModelForSeq2SeqLM.from_pretrained("Samuael/ethipic-sec2sec-tigre") - Notebooks
- Google Colab
- Kaggle
ethipic-sec2sec-tigrinya
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.1009
- eval_wer: 0.0416
- eval_cer: 0.0113
- eval_bleu: 91.6015
- eval_runtime: 30.3787
- eval_samples_per_second: 9.842
- eval_steps_per_second: 0.099
- epoch: 4.0
- step: 51000
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:
- learning_rate: 0.0002
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
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