Instructions to use lilferrit/5-e-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lilferrit/5-e-5 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lilferrit/5-e-5") model = AutoModelForSeq2SeqLM.from_pretrained("lilferrit/5-e-5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from lilferrit/5-e-5: direct link, hf CLI and curl.
- Browser
- Download file 1.45 kB
-
https://huggingface.co/lilferrit/5-e-5/resolve/main/README.md
- Command line
-
hf download hf://lilferrit/5-e-5/README.md
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curl -L -o README.md https://huggingface.co/lilferrit/5-e-5/resolve/main/README.md
1.45 kB
metadata
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: 5-e-5
results: []
5-e-5
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: 2.0497
- Bleu: 16.4909
- Gen Len: 28.2457
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 2.6169 | 0.0177 | 5000 | 2.0497 | 16.4909 | 28.2457 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1