---
license: cc-by-nc-nd-4.0
base_model: google/t5-efficient-base
tags:
- generated_from_trainer
model-index:
- name: checkpoint
  results: []
---

<!-- 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. -->

# how to use the model

```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("piazzola/test2")
model = AutoModelForSeq2SeqLM.from_pretrained("piazzola/test2")

from transformers import pipeline

pipe = pipeline("text2text-generation", model="piazzola/test2")

sentence = "i left the keys in the car."

output = pipe(sentence, max_new_tokens=100, do_sample=True, temperature=0.1)
print(output)
```

# checkpoint

This model is a fine-tuned version of [google/t5-efficient-base](https://huggingface.co/google/t5-efficient-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3070

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.3   | 240  | 1.4901          |
| No log        | 0.6   | 480  | 0.7750          |
| 3.5263        | 0.9   | 720  | 0.5219          |
| 3.5263        | 1.2   | 960  | 0.3782          |
| 0.607         | 1.5   | 1200 | 0.3521          |
| 0.607         | 1.8   | 1440 | 0.3356          |
| 0.4173        | 2.1   | 1680 | 0.3255          |
| 0.4173        | 2.4   | 1920 | 0.3151          |
| 0.368         | 2.7   | 2160 | 0.3093          |
| 0.368         | 3.0   | 2400 | 0.3070          |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2