transformers safetensors t5 text2text-generation generated_from_trainer base_model:google/t5-efficient-base license:cc-by-nc-nd-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
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
import torch

tokenizer = AutoTokenizer.from_pretrained("piazzola/test1")
model = AutoModelForSeq2SeqLM.from_pretrained("piazzola/test1")

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

with torch.no_grad():
    inputs = tokenizer([sentence], return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=100)
    generated_text = tokenizer.decode(outputs[0])

print(generated_text)
```

# 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: 0.1609

## 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: 4
- eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.3088        | 0.3   | 7458  | 0.2634          |
| 0.2615        | 0.6   | 14916 | 0.2143          |
| 0.2294        | 0.9   | 22374 | 0.1951          |
| 0.2137        | 1.2   | 29832 | 0.1830          |
| 0.1944        | 1.5   | 37290 | 0.1736          |
| 0.1918        | 1.8   | 44748 | 0.1682          |
| 0.18          | 2.1   | 52206 | 0.1659          |
| 0.1801        | 2.4   | 59664 | 0.1623          |
| 0.185         | 2.7   | 67122 | 0.1609          |


### Framework versions

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