Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use rlimonta/translation_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rlimonta/translation_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rlimonta/translation_model") model = AutoModelForSeq2SeqLM.from_pretrained("rlimonta/translation_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from rlimonta/translation_model: direct link, hf CLI and curl.
- Browser
- Download file 1.62 kB
-
https://huggingface.co/rlimonta/translation_model/resolve/main/README.md
- Command line
-
hf download hf://rlimonta/translation_model/README.md
-
curl -L -o README.md https://huggingface.co/rlimonta/translation_model/resolve/main/README.md
1.62 kB
metadata
library_name: transformers
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: translation_model
results: []
translation_model
This model is a fine-tuned version of google-t5/t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6012
- Bleu: 6.3252
- Gen Len: 18.3517
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 1.8685 | 1.0 | 6355 | 1.6239 | 6.128 | 18.3648 |
| 1.8289 | 2.0 | 12710 | 1.6012 | 6.3252 | 18.3517 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2