Instructions to use Verdiola/T5small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Verdiola/T5small with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Verdiola/T5small") model = AutoModelForSeq2SeqLM.from_pretrained("Verdiola/T5small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Verdiola/T5small: direct link, hf CLI and curl.
- Browser
- Download file 1.24 kB
-
https://huggingface.co/Verdiola/T5small/resolve/main/README.md
- Command line
-
hf download hf://Verdiola/T5small/README.md
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curl -L -o README.md https://huggingface.co/Verdiola/T5small/resolve/main/README.md
1.24 kB
metadata
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: T5small
results: []
T5small
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0001
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0021 | 1.0 | 19997 | 0.0001 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3