model update

README.md

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+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: en
+datasets:
+- lmqg/qag_squad
+pipeline_tag: text2text-generation
+tags:
+- questions and answers generation
+widget:
+- text: "generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
+  example_title: "Questions & Answers Generation Example 1" 
+model-index:
+- name: lmqg/flan-t5-small-squad-qag
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qag_squad
+      type: default
+      args: default
+    metrics:
+    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_bertscore_question_answer_generation
+      value: 92.3
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
+      type: qa_aligned_recall_bertscore_question_answer_generation
+      value: 91.71
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
+      type: qa_aligned_precision_bertscore_question_answer_generation
+      value: 92.92
+    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_moverscore_question_answer_generation
+      value: 63.74
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
+      type: qa_aligned_recall_moverscore_question_answer_generation
+      value: 62.2
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
+      type: qa_aligned_precision_moverscore_question_answer_generation
+      value: 65.5
+---
+
+# Model Card of `lmqg/flan-t5-small-squad-qag`
+This model is fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) for question & answer pair generation task on the [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+
+
+### Overview
+- **Language model:** [google/flan-t5-small](https://huggingface.co/google/flan-t5-small)   
+- **Language:** en  
+- **Training data:** [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (default)
+- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
+- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
+- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
+
+### Usage
+- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
+```python
+from lmqg import TransformersQG
+
+# initialize model
+model = TransformersQG(language="en", model="lmqg/flan-t5-small-squad-qag")
+
+# model prediction
+question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
+
+```
+
+- With `transformers`
+```python
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", "lmqg/flan-t5-small-squad-qag")
+output = pipe("generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
+
+```
+
+## Evaluation
+
+
+- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/flan-t5-small-squad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_squad.default.json) 
+
+|                                 |   Score | Type    | Dataset                                                          |
+|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
+| QAAlignedF1Score (BERTScore)    |   92.3  | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
+| QAAlignedF1Score (MoverScore)   |   63.74 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
+| QAAlignedPrecision (BERTScore)  |   92.92 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
+| QAAlignedPrecision (MoverScore) |   65.5  | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
+| QAAlignedRecall (BERTScore)     |   91.71 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
+| QAAlignedRecall (MoverScore)    |   62.2  | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) |
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qag_squad
+ - dataset_name: default
+ - input_types: ['paragraph']
+ - output_types: ['questions_answers']
+ - prefix_types: ['qag']
+ - model: google/flan-t5-small
+ - max_length: 512
+ - max_length_output: 256
+ - epoch: 14
+ - batch: 16
+ - lr: 0.0001
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 4
+ - label_smoothing: 0.0
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/flan-t5-small-squad-qag/raw/main/trainer_config.json).
+
+## Citation
+```
+@inproceedings{ushio-etal-2022-generative,
+    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
+    author = "Ushio, Asahi  and
+        Alva-Manchego, Fernando  and
+        Camacho-Collados, Jose",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, U.A.E.",
+    publisher = "Association for Computational Linguistics",
+}
+
+```

added_tokens.json

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+}

config.json

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+{
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+  "add_prefix": true,
+  "architectures": [
+    "T5ForConditionalGeneration"
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+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
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+  "dropout_rate": 0.1,
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+  "feed_forward_proj": "gated-gelu",
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+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 8,
+  "num_heads": 6,
+  "num_layers": 8,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.21.2",
+  "use_cache": true,
+  "vocab_size": 32101
+}

eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_squad.default.json

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special_tokens_map.json

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tokenizer_config.json

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trainer_config.json

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+{"dataset_path": "lmqg/qag_squad", "dataset_name": "default", "input_types": ["paragraph"], "output_types": ["questions_answers"], "prefix_types": ["qag"], "model": "google/flan-t5-small", "max_length": 512, "max_length_output": 256, "epoch": 14, "batch": 16, "lr": 0.0001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 4, "label_smoothing": 0.0}