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README.md
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@@ -31,33 +31,33 @@ model-index:
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metrics:
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- name: BLEU4 (Question Answering)
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type: bleu4_question_answering
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value:
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- name: ROUGE-L (Question Answering)
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type: rouge_l_question_answering
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value:
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- name: METEOR (Question Answering)
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type: meteor_question_answering
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value:
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- name: BERTScore (Question Answering)
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type: bertscore_question_answering
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value:
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- name: MoverScore (Question Answering)
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type: moverscore_question_answering
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value:
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- name: AnswerF1Score (Question Answering)
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type: answer_f1_score__question_answering
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value:
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- name: AnswerExactMatch (Question Answering)
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type: answer_exact_match_question_answering
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value:
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---
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# Model Card of `vocabtrimmer/mt5-small-trimmed-ru-120000-ruquad-qa`
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This model is fine-tuned version of [
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### Overview
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- **Language model:** [
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- **Language:** ru
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- **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (default)
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- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
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| | Score | Type | Dataset |
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|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
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| AnswerExactMatch |
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| AnswerF1Score |
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| BERTScore |
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| Bleu_1 |
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| Bleu_2 |
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| Bleu_3 |
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| Bleu_4 |
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| METEOR |
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| MoverScore |
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| ROUGE_L |
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@@ -114,12 +114,12 @@ The following hyperparameters were used during fine-tuning:
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- input_types: ['paragraph_question']
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- output_types: ['answer']
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- prefix_types: None
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- model:
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- max_length: 512
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- max_length_output: 32
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- epoch:
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- batch: 32
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- lr: 0.
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- fp16: False
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- random_seed: 1
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- gradient_accumulation_steps: 2
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metrics:
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- name: BLEU4 (Question Answering)
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type: bleu4_question_answering
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value: 29.71
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- name: ROUGE-L (Question Answering)
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type: rouge_l_question_answering
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value: 55.07
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- name: METEOR (Question Answering)
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type: meteor_question_answering
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value: 41.65
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- name: BERTScore (Question Answering)
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type: bertscore_question_answering
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value: 94.96
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- name: MoverScore (Question Answering)
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type: moverscore_question_answering
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value: 83.99
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- name: AnswerF1Score (Question Answering)
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type: answer_f1_score__question_answering
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value: 73.33
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- name: AnswerExactMatch (Question Answering)
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type: answer_exact_match_question_answering
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value: 51.37
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---
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# Model Card of `vocabtrimmer/mt5-small-trimmed-ru-120000-ruquad-qa`
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This model is fine-tuned version of [ckpts/mt5-small-trimmed-ru-120000](https://huggingface.co/ckpts/mt5-small-trimmed-ru-120000) for question answering task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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### Overview
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- **Language model:** [ckpts/mt5-small-trimmed-ru-120000](https://huggingface.co/ckpts/mt5-small-trimmed-ru-120000)
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- **Language:** ru
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- **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (default)
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- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
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| | Score | Type | Dataset |
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|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
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| AnswerExactMatch | 51.37 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
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| AnswerF1Score | 73.33 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
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| BERTScore | 94.96 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
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| Bleu_1 | 46.17 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
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| Bleu_2 | 40.21 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
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| Bleu_3 | 34.84 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
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| Bleu_4 | 29.71 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
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| METEOR | 41.65 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
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| MoverScore | 83.99 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
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| ROUGE_L | 55.07 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
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- input_types: ['paragraph_question']
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- output_types: ['answer']
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- prefix_types: None
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- model: ckpts/mt5-small-trimmed-ru-120000
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- max_length: 512
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- max_length_output: 32
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- epoch: 15
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- batch: 32
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- lr: 0.001
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- fp16: False
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- random_seed: 1
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- gradient_accumulation_steps: 2
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eval/metric.first.answer.paragraph_question.answer.lmqg_qg_ruquad.default.json
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{"validation": {"Bleu_1": 0.
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{"validation": {"Bleu_1": 0.48334806955494575, "Bleu_2": 0.42446150972912017, "Bleu_3": 0.37188475189985354, "Bleu_4": 0.32157021715601214, "METEOR": 0.42513802327329575, "ROUGE_L": 0.5669332394759723, "BERTScore": 0.953661481582806, "MoverScore": 0.8476297354040059, "AnswerF1Score": 75.26023674768254, "AnswerExactMatch": 53.4154090548054}, "test": {"Bleu_1": 0.4617225885020593, "Bleu_2": 0.40205062666505914, "Bleu_3": 0.34837286365826947, "Bleu_4": 0.2971343256502367, "METEOR": 0.4164766087640628, "ROUGE_L": 0.5506870736401595, "BERTScore": 0.9496106096470138, "MoverScore": 0.8398631419298846, "AnswerF1Score": 73.32765776541284, "AnswerExactMatch": 51.37013502779984}}
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eval/samples.test.hyp.paragraph_question.answer.lmqg_qg_ruquad.default.txt
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eval/samples.validation.hyp.paragraph_question.answer.lmqg_qg_ruquad.default.txt
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