Transformers
PyTorch
t5
text2text-generation
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use fernandohf/model_en_pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fernandohf/model_en_pt with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("fernandohf/model_en_pt") model = AutoModelForSeq2SeqLM.from_pretrained("fernandohf/model_en_pt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from fernandohf/model_en_pt: direct link, hf CLI and curl.
- Browser
- Download file 1.94 kB
-
https://huggingface.co/fernandohf/model_en_pt/resolve/main/README.md
- Command line
-
hf download hf://fernandohf/model_en_pt/README.md
-
curl -L -o README.md https://huggingface.co/fernandohf/model_en_pt/resolve/main/README.md
1.94 kB
| license: apache-2.0 | |
| base_model: t5-small | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - opus_books | |
| metrics: | |
| - bleu | |
| model-index: | |
| - name: model_en_pt | |
| results: | |
| - task: | |
| name: Sequence-to-sequence Language Modeling | |
| type: text2text-generation | |
| dataset: | |
| name: opus_books | |
| type: opus_books | |
| config: en-pt | |
| split: train | |
| args: en-pt | |
| metrics: | |
| - name: Bleu | |
| type: bleu | |
| value: 1.0547 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # model_en_pt | |
| This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 3.1561 | |
| - Bleu: 1.0547 | |
| - Gen Len: 17.8369 | |
| ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | |
| | No log | 1.0 | 79 | 3.4923 | 1.2907 | 17.2624 | | |
| | No log | 2.0 | 158 | 3.3099 | 0.8388 | 17.2482 | | |
| | No log | 3.0 | 237 | 3.2192 | 0.8526 | 17.7021 | | |
| | No log | 4.0 | 316 | 3.1709 | 0.9618 | 17.8298 | | |
| | No log | 5.0 | 395 | 3.1561 | 1.0547 | 17.8369 | | |
| ### Framework versions | |
| - Transformers 4.34.0 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.14.0 | |