Instructions to use anegi/t5smallmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anegi/t5smallmodel with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("anegi/t5smallmodel") model = AutoModelForSeq2SeqLM.from_pretrained("anegi/t5smallmodel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from anegi/t5smallmodel: direct link, hf CLI and curl.
- Browser
- Download file 1.34 kB
-
https://huggingface.co/anegi/t5smallmodel/resolve/main/README.md
- Command line
-
hf download hf://anegi/t5smallmodel/README.md
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curl -L -o README.md https://huggingface.co/anegi/t5smallmodel/resolve/main/README.md
1.34 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: t5smallmodel
results: []
t5smallmodel
This model is a fine-tuned version of t5-small on the samsum dataset. It achieves the following results on the evaluation set:
- Loss: 1.8672
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.1077 | 0.54 | 500 | 1.8672 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.10.3