piazzola/test1

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 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