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--- |
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language: uk |
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license: mit |
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tags: |
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- generated_from_trainer |
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widget: |
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- text: Що відправлять для ЗСУ? |
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context: Про це повідомив міністр оборони Арвідас Анушаускас. Уряд Литви не має |
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наміру зупинятися у військово-технічній допомозі Україні. Збройні сили отримають |
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антидрони, тепловізори та ударний безпілотник. «Незабаром Литва передасть Україні |
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не лише обіцяні бронетехніку, вантажівки та позашляховики, але також нову партію |
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антидронів та тепловізорів. І, звичайно, Байрактар, який придбають на зібрані |
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литовцями гроші», - написав глава Міноборони. |
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base_model: ukr-models/xlm-roberta-base-uk |
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model-index: |
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- name: ukrainian-qa |
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results: [] |
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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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# ukrainian-qa |
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This model is a fine-tuned version of [ukr-models/xlm-roberta-base-uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) on the [UA-SQuAD](https://github.com/fido-ai/ua-datasets/tree/main/ua_datasets/src/question_answering) dataset. |
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Link to training scripts - [https://github.com/robinhad/ukrainian-qa](https://github.com/robinhad/ukrainian-qa) |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4778 |
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## Model description |
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More information needed |
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## How to use |
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```python |
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from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering |
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model_name = "robinhad/ukrainian-qa" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForQuestionAnswering.from_pretrained(model_name) |
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qa_model = pipeline("question-answering", model=model.to("cpu"), tokenizer=tokenizer) |
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question = "Де ти живеш?" |
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context = "Мене звати Сара і я живу у Лондоні" |
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qa_model(question = question, context = context) |
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``` |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.4526 | 1.0 | 650 | 1.3631 | |
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| 1.3317 | 2.0 | 1300 | 1.2229 | |
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| 1.0693 | 3.0 | 1950 | 1.2184 | |
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| 0.6851 | 4.0 | 2600 | 1.3171 | |
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| 0.5594 | 5.0 | 3250 | 1.3893 | |
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| 0.4954 | 6.0 | 3900 | 1.4778 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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