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--- |
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tags: |
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- Question(s) Generation |
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metrics: |
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- rouge |
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model-index: |
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- name: consciousAI/question-generation-auto-hints-t5-v1-base-s-q |
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results: [] |
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--- |
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# Auto Question Generation |
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The model is intended to be used for Auto And/Or Hint enabled Question Generation tasks. The model is expected to produce one or possibly more than one question from the provided context. |
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[Live Demo: Question Generation](https://huggingface.co/spaces/consciousAI/question_generation) |
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Including this there are five models trained with different training sets, demo provide comparison to all in one go. However, you can reach individual projects at below links: |
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[Auto Question Generation v1](https://huggingface.co/consciousAI/question-generation-auto-t5-v1-base-s) |
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[Auto Question Generation v2](https://huggingface.co/consciousAI/question-generation-auto-t5-v1-base-s-q) |
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[Auto Question Generation v3](https://huggingface.co/consciousAI/question-generation-auto-t5-v1-base-s-q-c) |
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[Auto/Hints based Question Generation v2](https://huggingface.co/consciousAI/question-generation-auto-hints-t5-v1-base-s-q-c) |
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This model can be used as below: |
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``` |
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from transformers import ( |
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AutoModelForSeq2SeqLM, |
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AutoTokenizer |
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) |
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model_checkpoint = "consciousAI/question-generation-auto-hints-t5-v1-base-s-q" |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint) |
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tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) |
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## Input with prompt |
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context="question_context: <context>" |
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encodings = tokenizer.encode(context, return_tensors='pt', truncation=True, padding='max_length').to(device) |
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## You can play with many hyperparams to condition the output, look at demo |
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output = model.generate(encodings, |
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#max_length=300, |
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#min_length=20, |
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#length_penalty=2.0, |
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num_beams=4, |
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#early_stopping=True, |
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#do_sample=True, |
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#temperature=1.1 |
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) |
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## Multiple questions are expected to be delimited by '?' You can write a small wrapper to elegantly format. Look at the demo. |
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questions = [tokenizer.decode(id, clean_up_tokenization_spaces=False, skip_special_tokens=False) for id in output] |
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``` |
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## Training and evaluation data |
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Squad & QNLi combo. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 1.8298 | 1.0 | 14515 | 1.7529 | 0.3535 | 0.1825 | 0.3251 | 0.3294 | |
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| 1.4931 | 2.0 | 29030 | 1.7132 | 0.3558 | 0.1881 | 0.3267 | 0.3308 | |
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| 1.2756 | 3.0 | 43545 | 1.7579 | 0.3604 | 0.1901 | 0.3307 | 0.3345 | |
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| 1.0936 | 4.0 | 58060 | 1.8173 | 0.36 | 0.1901 | 0.3295 | 0.3334 | |
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| 0.955 | 5.0 | 72575 | 1.9204 | 0.3611 | 0.1884 | 0.3295 | 0.3336 | |
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| 0.8117 | 6.0 | 87090 | 2.0183 | 0.355 | 0.1836 | 0.3241 | 0.3282 | |
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| 0.6949 | 7.0 | 101605 | 2.1347 | 0.3556 | 0.1836 | 0.3242 | 0.3282 | |
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| 0.636 | 8.0 | 116120 | 2.2567 | 0.3568 | 0.1855 | 0.3248 | 0.3286 | |
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| 0.591 | 9.0 | 130635 | 2.3598 | 0.3563 | 0.1844 | 0.3238 | 0.3281 | |
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| 0.5417 | 10.0 | 145150 | 2.4725 | 0.3556 | 0.1828 | 0.3229 | 0.3269 | |
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### Framework versions |
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- Transformers 4.23.0.dev0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.5.2 |
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- Tokenizers 0.13.0 |
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