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--- |
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license: apache-2.0 |
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tags: |
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- text2text-generation |
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- generated_from_trainer |
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metrics: |
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- rouge |
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- bleu |
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datasets: |
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- domenicrosati/QA2D |
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model-index: |
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- name: QA2D-t5-small |
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results: |
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- task: |
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name: Question to Declarative Sentence |
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type: text2text-generation |
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dataset: |
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name: domenicrosati/QA2D |
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type: domenicrosati/QA2D |
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args: plain_text |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 89.8753 |
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- name: Rouge2 |
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type: rouge |
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value: 81.8104 |
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- name: Rougel |
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type: rouge |
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value: 85.4253 |
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- name: Rougelsum |
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type: rouge |
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value: 85.4236 |
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- name: Bleu |
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type: bleu |
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value: 72.1080 |
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widget: |
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- text: "where in the world is carmen sandiego. she is in abruzzo" |
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example_title: "Where is Carmen Sandiego?" |
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- text: "which province is halifax in. nova scotia" |
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example_title: "A Halifact" |
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--- |
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# QA2D-t5-small |
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This model is a fine-tuned version of [t5-small](https://huggingface.co./t5-small) on [QA2D](https://huggingface.co./datasets/domenicrosati/QA2D). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3236 |
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- Rouge1: 89.8753 |
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- Rouge2: 81.8104 |
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- Rougel: 85.4253 |
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- Rougelsum: 85.4236 |
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- Bleu: 72.1080 |
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See: [https://wandb.ai/domenicrosati/huggingface/runs/n1yallpe](https://wandb.ai/domenicrosati/huggingface/runs/n1yallpe) for training and eval stats and [https://github.com/domenicrosati/qa2d-models](https://github.com/domenicrosati/qa2d-models) for the code! |
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## Model description |
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A t5-model model to convert questions, answer pairs into statements. |
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Due to the way it's been trained the input should be all lower case and punctuation removed. |
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Use with `. ` as the seperator between question and answer. |
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> "where in the world is carmen. abruzzo" |
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> Output: "carmen is in abruzzo" |
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Thought punctation and upper case works. |
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``` |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained('domenicrosati/QA2D-t5-small') |
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model = AutoModelForSeq2SeqLM.from_pretrained('domenicrosati/QA2D-t5-small') |
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question = "where in the world is carmen sandiego" |
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answer = "she is in abruzzo" |
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SEP = ". " |
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prompt = f'{question}{SEP}{answer}' |
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input_ids = tokenizer(prompt, return_tensors='pt').input_ids |
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output_ids = model.generate(input_ids) |
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responses = tokenizer.batch_decode(output_ids, skip_special_tokens=True) |
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# ['carmen sandiego is in abruzzo'] |
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``` |
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## Intended uses & limitations |
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To convert questions, answer pairs into statements. |
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## Training and evaluation data |
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Uses [QA2D](https://huggingface.co./datasets/domenicrosati/QA2D). |
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See [https://github.com/domenicrosati/qa2d-models](https://github.com/domenicrosati/qa2d-models) |
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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: 5.6e-05 |
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- train_batch_size: 12 |
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- eval_batch_size: 12 |
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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: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | |
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|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 0.3177 | 1.0 | 5060 | 0.3144 | 89.6379 | 81.3168 | 85.2036 | 85.1904 | 71.4255 | |
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| 0.2479 | 2.0 | 10120 | 0.3035 | 89.7816 | 81.6556 | 85.3541 | 85.3406 | 71.7248 | |
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| 0.2268 | 3.0 | 15180 | 0.3015 | 89.8287 | 81.698 | 85.3434 | 85.3387 | 71.8344 | |
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| 0.2111 | 4.0 | 20240 | 0.3014 | 89.8082 | 81.7192 | 85.4094 | 85.406 | 71.9172 | |
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| 0.1991 | 5.0 | 25300 | 0.3023 | 89.8776 | 81.7607 | 85.3912 | 85.3842 | 71.9417 | |
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| 0.1886 | 6.0 | 30360 | 0.3012 | 89.901 | 81.7614 | 85.3345 | 85.3315 | 72.0218 | |
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| 0.1803 | 7.0 | 35420 | 0.3010 | 89.8776 | 81.8189 | 85.4154 | 85.4097 | 72.0533 | |
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| 0.1724 | 8.0 | 40480 | 0.3041 | 89.9168 | 81.8663 | 85.4457 | 85.4447 | 72.1470 | |
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| 0.1654 | 9.0 | 45540 | 0.3076 | 89.8901 | 81.8536 | 85.4857 | 85.4863 | 72.0830 | |
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| 0.1601 | 10.0 | 50600 | 0.3083 | 89.9186 | 81.881 | 85.4653 | 85.4594 | 72.1048 | |
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| 0.1546 | 11.0 | 55660 | 0.3136 | 89.8958 | 81.8533 | 85.4217 | 85.4238 | 72.0752 | |
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| 0.1502 | 12.0 | 60720 | 0.3138 | 89.903 | 81.8604 | 85.4301 | 85.4267 | 72.1373 | |
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| 0.1461 | 13.0 | 65780 | 0.3140 | 89.8867 | 81.7945 | 85.3698 | 85.3662 | 72.0718 | |
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| 0.1423 | 14.0 | 70840 | 0.3171 | 89.8985 | 81.8221 | 85.4348 | 85.4331 | 72.1168 | |
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| 0.1392 | 15.0 | 75900 | 0.3186 | 89.8938 | 81.8246 | 85.402 | 85.3991 | 72.0858 | |
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| 0.1366 | 16.0 | 80960 | 0.3208 | 89.859 | 81.8133 | 85.4194 | 85.4182 | 72.1014 | |
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| 0.1344 | 17.0 | 86020 | 0.3222 | 89.8909 | 81.828 | 85.4392 | 85.435 | 72.1380 | |
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| 0.1324 | 18.0 | 91080 | 0.3226 | 89.8906 | 81.8351 | 85.4506 | 85.4441 | 72.1622 | |
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| 0.1309 | 19.0 | 96140 | 0.3231 | 89.8925 | 81.8369 | 85.4375 | 85.4366 | 72.1552 | |
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| 0.1305 | 20.0 | 101200 | 0.3236 | 89.8753 | 81.8104 | 85.4253 | 85.4236 | 72.1080 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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