mrm8488/t5-small-finetuned-text2log

T5 (small) fine-tuned on Text2Log

This model is a fine-tuned version of t5-small on an Text2Log dataset. It achieves the following results on the evaluation set:

• Loss: 0.0313

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: 6

Training results

Training Loss	Epoch	Step	Validation Loss
0.0749	1.0	21661	0.0509
0.0564	2.0	43322	0.0396
0.0494	3.0	64983	0.0353
0.0425	4.0	86644	0.0332
0.04	5.0	108305	0.0320
0.0381	6.0	129966	0.0313

Usage:

from transformers import AutoTokenizer, T5ForConditionalGeneration

MODEL_CKPT = "mrm8488/t5-small-finetuned-text2log"

model = T5ForConditionalGeneration.from_pretrained(MODEL_CKPT).to(device)
tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT)

def translate(text):
    inputs = tokenizer(text, padding="longest", max_length=64, return_tensors="pt")
    input_ids = inputs.input_ids.to(device)
    attention_mask = inputs.attention_mask.to(device)

    output = model.generate(input_ids, attention_mask=attention_mask, early_stopping=False, max_length=64)

    return tokenizer.decode(output[0], skip_special_tokens=True)

prompt_nl_to_fol = "translate to fol: "
prompt_fol_to_nl = "translate to nl: "
example_1 = "Every killer leaves something."
example_2 = "all x1.(_woman(x1) -> exists x2.(_emotion(x2) & _experience(x1,x2)))"

print(translate(prompt_nl_to_fol + example_1)) # all x1.(_killer(x1) -> exists x2._leave(x1,x2))
print(translate(prompt_fol_to_nl + example_2)) # Every woman experiences emotions.

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.0+cu111
• Datasets 1.18.3
• Tokenizers 0.11.0