flan-t5-text2sparql-custom-tokenizer

This model is a fine-tuned version of google/flan-t5-base on the lc_quad dataset. It achieves the following results on the evaluation set:

  • Loss: 1.8039

Model description

This model uses the T5 tokenizer just for the input and a custom one for the SPARQL queries. This has lead to a dramatic improvement in performance, albeit not quite usable yet.

Intended uses & limitations

Because we used two different tokenizers, you cannot use this model simply in a pipeline. Use the following Python code as a starting point:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_checkpoint = "InfAI/flan-t5-text2sparql-custom-tokenizer"
question = "What was the population of Clermont-Ferrand on 1-1-2013?"
gold_answer = "SELECT ?obj WHERE { wd:Q42168 p:P1082 ?s . ?s ps:P1082 ?obj . ?s pq:P585 ?x filter(contains(YEAR(?x),'2013')) }"

model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)

tokenizer_in = AutoTokenizer.from_pretrained("google/flan-t5-base")
tokenizer_out = AutoTokenizer.from_pretrained("InfAI/sparql-tokenizer")

sample = f"Create SPARQL Query: {question}"

inputs = tokenizer_in([sample], return_tensors="pt")
outputs = model.generate(**inputs)

print(f"Gold answer: {gold_answer}")
print("       Model:" + tokenizer_out.decode(outputs[0], skip_special_tokens=True))
Gold answer: SELECT ?obj WHERE { wd:Q42168 p:P1082 ?s . ?s ps:P1082 ?obj . ?s pq:P585 ?x filter(contains(YEAR(?x),'2013'))
      Model: SELECT?obj WHERE { wd:Q4754 p:P1082?s.?s ps:P1082?obj.?s pq:P585?x filter(contains(YEAR(?x),'2013')) }

Common errors include:

  • A stray closed curly brace at the end
  • One of subject / predicate / object is wrong, while the other two are correct

Training and evaluation data

More information needed

Training procedure

We trained the model for 50 epochs, which was way over the top. The loss stagnates after about 25 epochs and looking manually at some examples from the validation set showed us that the queries do not improve beyond this point using these hyperparameters. We were aware that the number of epochs was probably too high, but our goal was to find out how many epochs were beneficial to the performance.

There are two avenues we will explore to get rid of these errors:

  • Continue training with different hyperparameters
  • Apply more preprocessing to the dataset

The results will be uploaded to this repo.

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss
No log 1.0 301 2.6503
3.2271 2.0 602 2.3894
3.2271 3.0 903 2.2532
2.3957 4.0 1204 2.1631
2.18 5.0 1505 2.0788
2.18 6.0 1806 2.0195
2.0209 7.0 2107 1.9681
2.0209 8.0 2408 1.9353
1.9087 9.0 2709 1.8936
1.8114 10.0 3010 1.8683
1.8114 11.0 3311 1.8556
1.7254 12.0 3612 1.8284
1.7254 13.0 3913 1.8099
1.6556 14.0 4214 1.7932
1.5891 15.0 4515 1.7823
1.5891 16.0 4816 1.7691
1.528 17.0 5117 1.7569
1.528 18.0 5418 1.7578
1.4784 19.0 5719 1.7561
1.4288 20.0 6020 1.7514
1.4288 21.0 6321 1.7372
1.3793 22.0 6622 1.7318
1.3793 23.0 6923 1.7244
1.3436 24.0 7224 1.7382
1.3073 25.0 7525 1.7254
1.3073 26.0 7826 1.7494
1.2692 27.0 8127 1.7378
1.2692 28.0 8428 1.7387
1.242 29.0 8729 1.7290
1.2107 30.0 9030 1.7391
1.2107 31.0 9331 1.7458
1.1817 32.0 9632 1.7528
1.1817 33.0 9933 1.7521
1.1661 34.0 10234 1.7672
1.136 35.0 10535 1.7594
1.136 36.0 10836 1.7564
1.1216 37.0 11137 1.7670
1.1216 38.0 11438 1.7724
1.1031 39.0 11739 1.7766
1.0834 40.0 12040 1.7756
1.0834 41.0 12341 1.7786
1.0707 42.0 12642 1.7947
1.0707 43.0 12943 1.7931
1.058 44.0 13244 1.7925
1.0489 45.0 13545 1.7939
1.0489 46.0 13846 1.7969
1.0421 47.0 14147 1.7982
1.0421 48.0 14448 1.7994
1.0357 49.0 14749 1.8018
1.03 50.0 15050 1.8039

Framework versions

  • Transformers 4.18.0
  • Pytorch 1.10.2+cu102
  • Datasets 2.4.0
  • Tokenizers 0.12.1
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Dataset used to train InfAI/flan-t5-text2sparql-custom-tokenizer