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bert-base-greek-uncased-v6-finetuned-polylex-mg

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Duplicate from snousias/bert-base-greek-uncased-v5-finetuned-polylex-mg

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  1. .gitattributes +35 -0
  2. .gitignore +1 -0
  3. README.md +601 -0
  4. config.json +26 -0
  5. generation_config.json +5 -0
  6. pytorch_model.bin +3 -0
  7. runs/Aug04_15-33-45_526b269448fb/events.out.tfevents.1691163235.526b269448fb.12888.0 +3 -0
  8. runs/Aug04_15-36-29_526b269448fb/events.out.tfevents.1691163400.526b269448fb.12888.1 +3 -0
  9. runs/Aug04_15-36-48_526b269448fb/events.out.tfevents.1691163414.526b269448fb.12888.2 +3 -0
  10. runs/Aug04_15-36-48_526b269448fb/events.out.tfevents.1691175372.526b269448fb.12888.3 +3 -0
  11. special_tokens_map.json +7 -0
  12. tokenizer.json +0 -0
  13. tokenizer_config.json +15 -0
  14. training_args.bin +3 -0
  15. vocab.txt +0 -0
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+ ---
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+ base_model: nlpaueb/bert-base-greek-uncased-v1
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: bert-base-greek-uncased-v5-finetuned-polylex-mg
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+ results: []
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+ duplicated_from: snousias/bert-base-greek-uncased-v5-finetuned-polylex-mg
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # bert-base-greek-uncased-v5-finetuned-polylex-mg
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+
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+ This model is a fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.3369
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+
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+ ## Model description
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+
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+ In this work we appropriately adapt a corpus of multiword expressions in Modern Greek, namely PolylexMG, characterised by the features detailed above formulating its spectrum of idiosyncrasy to finetune Greek BERT transformer model for masked language modelling classification and tasks. The GREEK-BERT model is pre-trained on free text corpora extracted from (a) the Greek part of Wikipedia, (b) the Greek part of the European Parliament Proceedings Parallel Corpus (Europarl), and (c) the Greek part of OSCAR (Koutsikakis et al, 2020:113), this monolingual model is based on the architecture of BERT-BASE-UNCASED. Specifically, Greek BERT has been finetuned with expressions derived from each syntactic category as they are described in PolylexMG (Fotopoulou et al., 2023) that includes 6,000 Greek lexical entries dataset entailing frozen idioms which are semantically fixed with no paradigmatic variation (Lamiroy, 2003) and light verb constructions in which the semantics are traced in the predicative noun and not the verb (Anastassiadis-Symeonidis et al., 2020).
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+
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+ ## Results, intended uses & limitations
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+
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+ This subsection presents the experimental evaluation results for the MWE-fine-tuned Greek BERT model with respect to classification use case. The derived setup assumes that raw text in modern Greek that may contain multiple sentences is processed by the language model and reports class with regards to whether the text segment contains multiword stereotypical expressions or not. We compared the fine-tuned BERT model with a baseline logistic regression model. The latter is using as input the same word embeddings as the MWE-fine-tuned BERT model.
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+ Greek-MWE-Bert was trained in a masked language model setting with full-expression-subdataset. The model perplexity was measured tο 303.21 before finetuning and 3.81 after finetuning demonstrating that the model has gained domain knowledge on multiword expressions. Qualitative outcomes presented in the following tables demonstrating the model performance in the case of 16 verbal constructs. The qualitative evaluation demonstrates that the fine-tuned model in all cases generates stereotypical multiword expressions while the original Greek BERT yields incomplete free-text related parts of sentences.
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+
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+ The finetuned model was further finetuned using for classification-oriented architecture with the classification-task-subdataset. The Bert classifier demonstrates an accuracy equivalent to 80% with a higher precision for free text reaching 80% and lower precision of 79% for MWE. In comparison the baseline classifier yields 70% for the free text and 67% for the MWE. We can observe that the two models have only 10% difference in accuracy despite the simplicity of baseline classifier. We can explain the small difference in the trained GreekBERT tokenizer that is used by both our model and the simplistic logistic regression model. However, the MWE-finetuned-Greek-BERT model can better capture sentences that contain MWEs due to the inherent benefits that the architecture offers.
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+
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+
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+ ## Training and evaluation data
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+
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+ ### Sample of PolylexMG full expression subdataset
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+
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+ |text |label|
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+ |-----------------------|-----|
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+ |αδειάζω τη γωνιά σε |1 |
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+ |αδειάζω πιστόλι πάνω σε|1 |
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+ |αλλάζω τον αδόξαστο σε |1 |
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+ |αλλάζω την πίστη σε |1 |
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+ |δεν αλλάζω ούτε κόμα σε|1 |
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+ |αλλάζω λόγια με |1 |
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+ |αλλάζω κουβέντες με |1 |
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+ |αλλάζω τα μυαλά σε |1 |
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+ |αλλάζω τα φώτα σε |1 |
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+ |αλλάζω τα πετρέλαια σε |1 |
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+ |αλλάζω τα πέταλα σε |1 |
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+
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+
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+ ### Sample of PolylexMG classification subdataset
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+
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+ |text |label|
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+ |-----------------------|-----|
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+ |Μέσα σε λίγα λεπτά άναψαν τα αίματα και ο διαπληκτισμός άρχισε να γίνεται όλο και πιο έντονος|1 |
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+ |Η πρώτη έκπληξη ήρθε αμέσως μόλις άναψαν τα τέσσερα κόκκινα φανάρια και το ένα πράσινο|0 |
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+ |Γιατί τα κάνετε αυτά, για να γελάνε οι άλλοι μαζί μας;|0 |
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+ |Κάθε φορά που έμπαινε καλάθι, έβγαζαν τις ίδιες ακριβώς ιαχές για να πάει γούρι και να μην κόψει η μαγιονέζα|1 |
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+ |Η νέα πυρκαγιά ξεκινά από την πίσω πλευρά του Πεντελικού Όρους, σε σημείο που δεν είχε καεί|0 |
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-06
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+ - train_batch_size: 512
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+ - eval_batch_size: 512
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 500
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+
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+
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+ ### Training results (Summary)
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:----:|:---------------:|
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+ | 5.2105 | 1.0 | 13 | 4.4870 |
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+ | 4.4319 | 2.0 | 26 | 3.8456 |
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+ | 4.0318 | 3.0 | 39 | 3.4164 |
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+ | 3.7558 | 4.0 | 52 | 3.2849 |
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+ | 1.1307 | 497.0 | 6461 | 1.3311 |
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+ | 1.1163 | 498.0 | 6474 | 1.3016 |
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+ | 1.099 | 499.0 | 6487 | 1.3532 |
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+ | 1.1246 | 500.0 | 6500 | 1.2222 |
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+
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+ ### Framework versions
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+
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+ - Transformers 4.31.0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.3
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+ - Tokenizers 0.13.3
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+
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+
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+ ### Training results (Full)
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:----:|:---------------:|
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+ | 5.2105 | 1.0 | 13 | 4.4870 |
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+ | 4.4319 | 2.0 | 26 | 3.8456 |
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+ | 4.0318 | 3.0 | 39 | 3.4164 |
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+ | 3.7558 | 4.0 | 52 | 3.2849 |
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+ | 3.5626 | 5.0 | 65 | 3.3146 |
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+ | 3.4355 | 6.0 | 78 | 3.1532 |
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+ | 3.3299 | 7.0 | 91 | 3.0451 |
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+ | 3.2313 | 8.0 | 104 | 2.9359 |
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+ | 3.1758 | 9.0 | 117 | 2.8543 |
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+ | 3.0762 | 10.0 | 130 | 2.8034 |
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+ | 3.0318 | 11.0 | 143 | 2.7975 |
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+ | 2.9481 | 12.0 | 156 | 2.6439 |
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+ | 2.8848 | 13.0 | 169 | 2.6623 |
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+ | 2.9002 | 14.0 | 182 | 2.6425 |
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+ | 2.8435 | 15.0 | 195 | 2.6639 |
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+ | 2.8451 | 16.0 | 208 | 2.6203 |
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+ | 2.7987 | 17.0 | 221 | 2.5597 |
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+ | 2.7522 | 18.0 | 234 | 2.5719 |
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+ | 2.7194 | 19.0 | 247 | 2.6220 |
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+ | 2.6923 | 20.0 | 260 | 2.5566 |
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+ | 2.678 | 21.0 | 273 | 2.4172 |
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+ | 2.6612 | 22.0 | 286 | 2.5726 |
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+ | 2.6272 | 23.0 | 299 | 2.4478 |
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+ | 2.6052 | 24.0 | 312 | 2.4366 |
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+ | 2.5694 | 25.0 | 325 | 2.3694 |
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+ | 2.593 | 26.0 | 338 | 2.4324 |
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+ | 2.548 | 27.0 | 351 | 2.4070 |
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+ | 2.4954 | 28.0 | 364 | 2.3651 |
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+ | 2.5097 | 29.0 | 377 | 2.3268 |
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+ | 2.5041 | 30.0 | 390 | 2.4208 |
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+ | 2.4919 | 31.0 | 403 | 2.4321 |
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+ | 2.461 | 32.0 | 416 | 2.3477 |
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+ | 2.4698 | 33.0 | 429 | 2.4017 |
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+ | 2.4557 | 34.0 | 442 | 2.3050 |
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+ | 2.4464 | 35.0 | 455 | 2.3282 |
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+ | 2.4215 | 36.0 | 468 | 2.3339 |
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+ | 2.4037 | 37.0 | 481 | 2.2429 |
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+ | 2.386 | 38.0 | 494 | 2.3452 |
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+ | 2.3961 | 39.0 | 507 | 2.3312 |
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+ | 2.3985 | 40.0 | 520 | 2.2921 |
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+ | 2.3302 | 41.0 | 533 | 2.2711 |
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+ | 2.3128 | 42.0 | 546 | 2.2344 |
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+ | 2.3158 | 43.0 | 559 | 2.1982 |
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+ | 2.2927 | 44.0 | 572 | 2.1473 |
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+ | 2.3122 | 45.0 | 585 | 2.2317 |
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+ | 2.2885 | 46.0 | 598 | 2.2060 |
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+ | 2.2592 | 47.0 | 611 | 2.1943 |
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+ | 2.2492 | 48.0 | 624 | 2.2361 |
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+ | 2.2495 | 49.0 | 637 | 2.2059 |
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+ | 2.2402 | 50.0 | 650 | 2.1461 |
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+ | 2.241 | 51.0 | 663 | 2.2181 |
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+ | 2.211 | 52.0 | 676 | 2.0885 |
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+ | 2.2165 | 53.0 | 689 | 2.1567 |
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+ | 2.2063 | 54.0 | 702 | 2.2112 |
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+ | 2.1715 | 55.0 | 715 | 2.2934 |
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+ | 2.1601 | 56.0 | 728 | 2.0745 |
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+ | 2.1796 | 57.0 | 741 | 2.1070 |
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+ | 2.152 | 58.0 | 754 | 2.0930 |
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+ | 2.1562 | 59.0 | 767 | 2.1106 |
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+ | 2.125 | 60.0 | 780 | 2.1529 |
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+ | 2.1318 | 61.0 | 793 | 2.0296 |
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+ | 2.1194 | 62.0 | 806 | 2.0323 |
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+ | 2.1396 | 63.0 | 819 | 1.9835 |
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+ | 2.1108 | 64.0 | 832 | 2.0066 |
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+ | 2.0874 | 65.0 | 845 | 1.9062 |
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+ | 2.0754 | 66.0 | 858 | 2.1728 |
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+ | 2.0928 | 67.0 | 871 | 2.0197 |
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+ | 2.0835 | 68.0 | 884 | 2.0767 |
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+ | 2.0684 | 69.0 | 897 | 2.1482 |
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+ | 2.0505 | 70.0 | 910 | 2.0667 |
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+ | 2.0564 | 71.0 | 923 | 2.1489 |
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+ | 2.0478 | 72.0 | 936 | 2.0015 |
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+ | 2.0478 | 73.0 | 949 | 1.9215 |
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+ | 2.0316 | 74.0 | 962 | 2.0238 |
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+ | 2.0171 | 75.0 | 975 | 2.0014 |
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+ | 2.0248 | 76.0 | 988 | 2.0775 |
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+ | 2.0066 | 77.0 | 1001 | 2.0390 |
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+ | 2.0018 | 78.0 | 1014 | 2.0043 |
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+ | 1.9925 | 79.0 | 1027 | 2.0138 |
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+ | 1.9614 | 80.0 | 1040 | 1.9499 |
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+ | 1.9877 | 81.0 | 1053 | 1.9642 |
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+ | 1.9499 | 82.0 | 1066 | 1.9676 |
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+ | 1.932 | 83.0 | 1079 | 1.9332 |
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+ | 1.9353 | 84.0 | 1092 | 1.8787 |
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+ | 1.9672 | 85.0 | 1105 | 1.9720 |
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+ | 1.9313 | 86.0 | 1118 | 1.9343 |
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+ | 1.9292 | 87.0 | 1131 | 1.8964 |
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+ | 1.9277 | 88.0 | 1144 | 1.9619 |
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+ | 1.9158 | 89.0 | 1157 | 1.9608 |
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+ | 1.921 | 90.0 | 1170 | 1.9171 |
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+ | 1.9191 | 91.0 | 1183 | 1.8871 |
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+ | 1.8935 | 92.0 | 1196 | 1.8857 |
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+ | 1.8818 | 93.0 | 1209 | 1.8909 |
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+ | 1.8782 | 94.0 | 1222 | 1.8951 |
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+ | 1.9028 | 95.0 | 1235 | 1.9164 |
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+ | 1.8907 | 96.0 | 1248 | 1.9650 |
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+ | 1.8626 | 97.0 | 1261 | 1.8906 |
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+ | 1.8413 | 98.0 | 1274 | 1.8957 |
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+ | 1.854 | 99.0 | 1287 | 1.9644 |
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+ | 1.8608 | 100.0 | 1300 | 1.8329 |
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+ | 1.8623 | 101.0 | 1313 | 1.8693 |
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+ | 1.7798 | 102.0 | 1326 | 1.8913 |
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+ | 1.846 | 103.0 | 1339 | 1.7854 |
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+ | 1.7972 | 104.0 | 1352 | 1.8611 |
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+ | 1.8443 | 105.0 | 1365 | 1.8482 |
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+ | 1.791 | 106.0 | 1378 | 1.7168 |
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+ | 1.7879 | 107.0 | 1391 | 1.8093 |
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+ | 1.7886 | 108.0 | 1404 | 1.8924 |
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+ | 1.8192 | 109.0 | 1417 | 1.7715 |
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+ | 1.7919 | 110.0 | 1430 | 1.7415 |
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+ | 1.7581 | 111.0 | 1443 | 1.7956 |
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+ | 1.7873 | 112.0 | 1456 | 1.7213 |
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+ | 1.7873 | 113.0 | 1469 | 1.7340 |
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+ | 1.7764 | 114.0 | 1482 | 1.8535 |
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+ | 1.7612 | 115.0 | 1495 | 1.8554 |
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+ | 1.7737 | 116.0 | 1508 | 1.8126 |
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+ | 1.7416 | 117.0 | 1521 | 1.8327 |
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+ | 1.7648 | 118.0 | 1534 | 1.6832 |
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+ | 1.7262 | 119.0 | 1547 | 1.6972 |
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+ | 1.7334 | 120.0 | 1560 | 1.7930 |
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+ | 1.7172 | 121.0 | 1573 | 1.6962 |
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+ | 1.7282 | 122.0 | 1586 | 1.8800 |
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+ | 1.7038 | 123.0 | 1599 | 1.7828 |
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+ | 1.6935 | 124.0 | 1612 | 1.7646 |
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+ | 1.758 | 125.0 | 1625 | 1.8069 |
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+ | 1.7018 | 126.0 | 1638 | 1.6958 |
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+ | 1.6886 | 127.0 | 1651 | 1.6692 |
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+ | 1.7004 | 128.0 | 1664 | 1.7256 |
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+ | 1.6947 | 129.0 | 1677 | 1.7587 |
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+ | 1.6897 | 130.0 | 1690 | 1.7484 |
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+ | 1.7037 | 131.0 | 1703 | 1.8455 |
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+ | 1.6981 | 132.0 | 1716 | 1.7588 |
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+ | 1.6828 | 133.0 | 1729 | 1.7421 |
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+ | 1.6596 | 134.0 | 1742 | 1.6933 |
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+ | 1.6782 | 135.0 | 1755 | 1.7040 |
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+ | 1.6595 | 136.0 | 1768 | 1.6705 |
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+ | 1.6567 | 137.0 | 1781 | 1.7744 |
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+ | 1.6588 | 138.0 | 1794 | 1.6545 |
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+ | 1.6225 | 139.0 | 1807 | 1.7576 |
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+ | 1.6394 | 140.0 | 1820 | 1.7256 |
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+ | 1.6515 | 141.0 | 1833 | 1.6668 |
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+ | 1.6335 | 144.0 | 1872 | 1.7098 |
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+ | 1.6501 | 145.0 | 1885 | 1.6671 |
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+ | 1.6192 | 146.0 | 1898 | 1.7073 |
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+ | 1.6198 | 147.0 | 1911 | 1.6653 |
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+ | 1.6182 | 148.0 | 1924 | 1.6723 |
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+ | 1.6172 | 149.0 | 1937 | 1.7293 |
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+ | 1.6129 | 150.0 | 1950 | 1.6545 |
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+ | 1.6054 | 151.0 | 1963 | 1.6850 |
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+ | 1.5967 | 152.0 | 1976 | 1.7064 |
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+ | 1.6028 | 153.0 | 1989 | 1.5292 |
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+ | 1.6156 | 154.0 | 2002 | 1.6477 |
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+ | 1.5965 | 155.0 | 2015 | 1.6110 |
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+ | 1.5695 | 156.0 | 2028 | 1.7071 |
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+ | 1.5586 | 157.0 | 2041 | 1.6504 |
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+ | 1.561 | 158.0 | 2054 | 1.6147 |
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+ | 1.5643 | 159.0 | 2067 | 1.6941 |
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+ | 1.5797 | 160.0 | 2080 | 1.7398 |
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+ | 1.5609 | 161.0 | 2093 | 1.5761 |
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+ | 1.5465 | 162.0 | 2106 | 1.6003 |
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+ | 1.5467 | 163.0 | 2119 | 1.5839 |
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+ | 1.5935 | 164.0 | 2132 | 1.6530 |
264
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265
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266
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267
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268
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269
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270
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271
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272
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273
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274
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275
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276
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277
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278
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279
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280
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281
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282
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283
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284
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285
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286
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287
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288
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289
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290
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291
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292
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293
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294
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295
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296
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297
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298
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299
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300
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301
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302
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303
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304
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305
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306
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307
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308
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309
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310
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311
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312
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313
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314
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315
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316
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317
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318
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319
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320
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321
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322
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323
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324
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325
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326
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327
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328
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329
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330
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331
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332
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333
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334
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335
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336
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337
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338
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339
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340
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341
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342
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343
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344
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345
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346
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347
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348
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349
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350
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351
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352
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353
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354
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355
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356
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357
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358
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359
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360
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361
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362
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363
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364
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365
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366
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367
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368
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369
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370
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371
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372
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373
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374
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375
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376
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377
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378
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379
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
+ | 1.1353 | 458.0 | 5954 | 1.3579 |
558
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559
+ | 1.1411 | 460.0 | 5980 | 1.2397 |
560
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561
+ | 1.14 | 462.0 | 6006 | 1.2439 |
562
+ | 1.11 | 463.0 | 6019 | 1.1894 |
563
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564
+ | 1.1052 | 465.0 | 6045 | 1.2281 |
565
+ | 1.1092 | 466.0 | 6058 | 1.2853 |
566
+ | 1.1229 | 467.0 | 6071 | 1.2988 |
567
+ | 1.1209 | 468.0 | 6084 | 1.3058 |
568
+ | 1.1147 | 469.0 | 6097 | 1.2705 |
569
+ | 1.1228 | 470.0 | 6110 | 1.2435 |
570
+ | 1.1124 | 471.0 | 6123 | 1.2188 |
571
+ | 1.0922 | 472.0 | 6136 | 1.2892 |
572
+ | 1.1228 | 473.0 | 6149 | 1.2250 |
573
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574
+ | 1.1295 | 475.0 | 6175 | 1.2126 |
575
+ | 1.1105 | 476.0 | 6188 | 1.3032 |
576
+ | 1.1223 | 477.0 | 6201 | 1.2190 |
577
+ | 1.1487 | 478.0 | 6214 | 1.2728 |
578
+ | 1.1288 | 479.0 | 6227 | 1.3258 |
579
+ | 1.1398 | 480.0 | 6240 | 1.2114 |
580
+ | 1.1127 | 481.0 | 6253 | 1.2695 |
581
+ | 1.135 | 482.0 | 6266 | 1.3376 |
582
+ | 1.106 | 483.0 | 6279 | 1.2860 |
583
+ | 1.0978 | 484.0 | 6292 | 1.3001 |
584
+ | 1.1254 | 485.0 | 6305 | 1.3180 |
585
+ | 1.1117 | 486.0 | 6318 | 1.3036 |
586
+ | 1.1249 | 487.0 | 6331 | 1.2380 |
587
+ | 1.1111 | 488.0 | 6344 | 1.3112 |
588
+ | 1.119 | 489.0 | 6357 | 1.2587 |
589
+ | 1.1203 | 490.0 | 6370 | 1.2867 |
590
+ | 1.1195 | 491.0 | 6383 | 1.3153 |
591
+ | 1.1304 | 492.0 | 6396 | 1.2762 |
592
+ | 1.1268 | 493.0 | 6409 | 1.2757 |
593
+ | 1.1478 | 494.0 | 6422 | 1.2493 |
594
+ | 1.1527 | 495.0 | 6435 | 1.2793 |
595
+ | 1.1252 | 496.0 | 6448 | 1.2435 |
596
+ | 1.1307 | 497.0 | 6461 | 1.3311 |
597
+ | 1.1163 | 498.0 | 6474 | 1.3016 |
598
+ | 1.099 | 499.0 | 6487 | 1.3532 |
599
+ | 1.1246 | 500.0 | 6500 | 1.2222 |
600
+
601
+
config.json ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "nlpaueb/bert-base-greek-uncased-v1",
3
+ "architectures": [
4
+ "BertForMaskedLM"
5
+ ],
6
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