model update
Browse files- .gitattributes +1 -0
- README.md +144 -0
- added_tokens.json +3 -0
- config.json +33 -0
- eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json +1 -0
- eval/samples.test.hyp.paragraph_sentence.answer.lmqg_qg_squad.default.txt +0 -0
- eval/samples.validation.hyp.paragraph_sentence.answer.lmqg_qg_squad.default.txt +0 -0
- generation_config.json +7 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +8 -0
- spiece.model +3 -0
- tokenizer.json +3 -0
- tokenizer_config.json +113 -0
- trainer_config.json +1 -0
.gitattributes
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@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: cc-by-4.0
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metrics:
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- bleu4
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- meteor
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- rouge-l
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- bertscore
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- moverscore
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language: en
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datasets:
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- lmqg/qg_squad
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pipeline_tag: text2text-generation
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tags:
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- answer extraction
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widget:
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- text: "extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress."
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example_title: "Answering Extraction Example 1"
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- text: "extract answers: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress. <hl>"
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example_title: "Answering Extraction Example 2"
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model-index:
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- name: lmqg/flan-t5-large-squad-ae
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results:
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- task:
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name: Text2text Generation
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type: text2text-generation
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dataset:
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name: lmqg/qg_squad
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type: default
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args: default
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metrics:
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- name: BLEU4 (Answer Extraction)
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type: bleu4_answer_extraction
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value: 42.39
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- name: ROUGE-L (Answer Extraction)
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type: rouge_l_answer_extraction
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value: 68.28
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- name: METEOR (Answer Extraction)
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type: meteor_answer_extraction
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value: 42.88
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- name: BERTScore (Answer Extraction)
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type: bertscore_answer_extraction
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value: 91.47
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- name: MoverScore (Answer Extraction)
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type: moverscore_answer_extraction
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value: 81.38
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- name: AnswerF1Score (Answer Extraction)
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type: answer_f1_score__answer_extraction
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value: 68.78
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- name: AnswerExactMatch (Answer Extraction)
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type: answer_exact_match_answer_extraction
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value: 57.12
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---
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# Model Card of `lmqg/flan-t5-large-squad-ae`
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This model is fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) for answer extraction on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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### Overview
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- **Language model:** [google/flan-t5-large](https://huggingface.co/google/flan-t5-large)
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- **Language:** en
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- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
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- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
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- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
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- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
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### Usage
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- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
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```python
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from lmqg import TransformersQG
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# initialize model
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model = TransformersQG(language="en", model="lmqg/flan-t5-large-squad-ae")
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# model prediction
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answers = model.generate_a("William Turner was an English painter who specialised in watercolour landscapes")
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```
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- With `transformers`
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```python
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from transformers import pipeline
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pipe = pipeline("text2text-generation", "lmqg/flan-t5-large-squad-ae")
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output = pipe("extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.")
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```
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## Evaluation
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- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/flan-t5-large-squad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json)
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| | Score | Type | Dataset |
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|:-----------------|--------:|:--------|:---------------------------------------------------------------|
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| AnswerExactMatch | 57.12 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| AnswerF1Score | 68.78 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| BERTScore | 91.47 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| Bleu_1 | 55.25 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| Bleu_2 | 50.75 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| Bleu_3 | 46.28 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| Bleu_4 | 42.39 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| METEOR | 42.88 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| MoverScore | 81.38 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| ROUGE_L | 68.28 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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## Training hyperparameters
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The following hyperparameters were used during fine-tuning:
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- dataset_path: lmqg/qg_squad
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- dataset_name: default
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- input_types: ['paragraph_sentence']
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- output_types: ['answer']
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- prefix_types: ['ae']
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- model: google/flan-t5-large
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- max_length: 512
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- max_length_output: 32
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- epoch: 8
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- batch: 4
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- lr: 0.0001
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- fp16: False
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- random_seed: 1
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- gradient_accumulation_steps: 8
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- label_smoothing: 0.0
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The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/flan-t5-large-squad-ae/raw/main/trainer_config.json).
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## Citation
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```
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@inproceedings{ushio-etal-2022-generative,
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title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
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author = "Ushio, Asahi and
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Alva-Manchego, Fernando and
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Camacho-Collados, Jose",
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
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month = dec,
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year = "2022",
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address = "Abu Dhabi, U.A.E.",
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publisher = "Association for Computational Linguistics",
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}
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```
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added_tokens.json
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{
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"<hl>": 32100
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}
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config.json
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{
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"_name_or_path": "lmqg_output/flan-t5-large-squad-ae/model_mzgdpa/epoch_2",
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"add_prefix": true,
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"d_ff": 2816,
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"d_kv": 64,
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"d_model": 1024,
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"decoder_start_token_id": 0,
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"dense_act_fn": "gelu_new",
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"feed_forward_proj": "gated-gelu",
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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"is_gated_act": true,
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"n_positions": 512,
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"num_decoder_layers": 24,
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"num_heads": 16,
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"num_layers": 24,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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"relative_attention_num_buckets": 32,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.26.1",
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"use_cache": true,
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"vocab_size": 32101
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}
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eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json
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{"validation": {"Bleu_1": 0.5183239253147955, "Bleu_2": 0.47660074214285897, "Bleu_3": 0.43560889670499786, "Bleu_4": 0.3989394771794267, "METEOR": 0.40463126856111037, "ROUGE_L": 0.642774684702172, "BERTScore": 0.9113678335579589, "MoverScore": 0.7826940169791669, "AnswerF1Score": 64.90831938054403, "AnswerExactMatch": 50.397350993377486}, "test": {"Bleu_1": 0.5524845189548288, "Bleu_2": 0.5074974218276695, "Bleu_3": 0.4628480781243989, "Bleu_4": 0.4238796645592996, "METEOR": 0.4288122621555011, "ROUGE_L": 0.6827541445091992, "BERTScore": 0.914656110998054, "MoverScore": 0.8137888475877497, "AnswerF1Score": 68.77802855838759, "AnswerExactMatch": 57.11880104403469}}
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eval/samples.test.hyp.paragraph_sentence.answer.lmqg_qg_squad.default.txt
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eval/samples.validation.hyp.paragraph_sentence.answer.lmqg_qg_squad.default.txt
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generation_config.json
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{
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"_from_model_config": true,
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"decoder_start_token_id": 0,
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"eos_token_id": 1,
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"pad_token_id": 0,
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"transformers_version": "4.26.1"
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:d93665ac789ff360f1dbb73d1303b07e8848de54578f6232d1a133637950af0e
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size 3132568549
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<hl>"
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],
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"eos_token": "</s>",
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"pad_token": "<pad>",
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}
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spiece.model
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version https://git-lfs.github.com/spec/v1
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size 791656
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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size 2422344
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tokenizer_config.json
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"<extra_id_0>",
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"<extra_id_1>",
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"<extra_id_11>",
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"<extra_id_17>",
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"<extra_id_18>",
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+
"<extra_id_19>",
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"<extra_id_23>",
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"<extra_id_27>",
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"<extra_id_28>",
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+
"<extra_id_29>",
|
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+
"<extra_id_30>",
|
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+
"<extra_id_31>",
|
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+
"<extra_id_32>",
|
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"<extra_id_33>",
|
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"<extra_id_34>",
|
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"<extra_id_35>",
|
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"<extra_id_36>",
|
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"<extra_id_37>",
|
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|
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"<extra_id_39>",
|
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"<extra_id_40>",
|
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"<extra_id_41>",
|
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|
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|
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"<extra_id_44>",
|
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"<extra_id_45>",
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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"<extra_id_66>",
|
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"<extra_id_67>",
|
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"<extra_id_68>",
|
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|
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"<extra_id_70>",
|
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"<extra_id_71>",
|
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"<extra_id_72>",
|
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"<extra_id_73>",
|
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"<extra_id_74>",
|
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"<extra_id_75>",
|
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+
"<extra_id_76>",
|
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"<extra_id_77>",
|
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"<extra_id_78>",
|
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"<extra_id_79>",
|
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"<extra_id_80>",
|
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+
"<extra_id_81>",
|
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"<extra_id_82>",
|
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"<extra_id_83>",
|
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+
"<extra_id_84>",
|
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+
"<extra_id_85>",
|
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+
"<extra_id_86>",
|
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+
"<extra_id_87>",
|
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+
"<extra_id_88>",
|
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+
"<extra_id_89>",
|
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+
"<extra_id_90>",
|
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+
"<extra_id_91>",
|
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+
"<extra_id_92>",
|
96 |
+
"<extra_id_93>",
|
97 |
+
"<extra_id_94>",
|
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+
"<extra_id_95>",
|
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+
"<extra_id_96>",
|
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+
"<extra_id_97>",
|
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+
"<extra_id_98>",
|
102 |
+
"<extra_id_99>"
|
103 |
+
],
|
104 |
+
"eos_token": "</s>",
|
105 |
+
"extra_ids": 100,
|
106 |
+
"model_max_length": 512,
|
107 |
+
"name_or_path": "lmqg_output/flan-t5-large-squad-ae/model_mzgdpa/epoch_2",
|
108 |
+
"pad_token": "<pad>",
|
109 |
+
"sp_model_kwargs": {},
|
110 |
+
"special_tokens_map_file": "/home/younes_huggingface_co/.cache/huggingface/hub/models--google--t5-v1_1-large/snapshots/314bc112b191ec17b625ba81438dc73d6c23659d/special_tokens_map.json",
|
111 |
+
"tokenizer_class": "T5Tokenizer",
|
112 |
+
"unk_token": "<unk>"
|
113 |
+
}
|
trainer_config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"dataset_path": "lmqg/qg_squad", "dataset_name": "default", "input_types": ["paragraph_sentence"], "output_types": ["answer"], "prefix_types": ["ae"], "model": "google/flan-t5-large", "max_length": 512, "max_length_output": 32, "epoch": 8, "batch": 4, "lr": 0.0001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 8, "label_smoothing": 0.0}
|