test1

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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

Framework versions

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