model update

README.md

@@ -0,0 +1,144 @@
+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: en
+datasets:
+- lmqg/qg_squad
+pipeline_tag: text2text-generation
+tags:
+- answer extraction
+widget:
+- 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."
+  example_title: "Answering Extraction Example 1" 
+- 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>"
+  example_title: "Answering Extraction Example 2" 
+model-index:
+- name: lmqg/t5-large-squad-ae
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qg_squad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4 (Answer Extraction)
+      type: bleu4_answer_extraction
+      value: 55.66
+    - name: ROUGE-L (Answer Extraction)
+      type: rouge_l_answer_extraction
+      value: 69.67
+    - name: METEOR (Answer Extraction)
+      type: meteor_answer_extraction
+      value: 43.06
+    - name: BERTScore (Answer Extraction)
+      type: bertscore_answer_extraction
+      value: 91.91
+    - name: MoverScore (Answer Extraction)
+      type: moverscore_answer_extraction
+      value: 82.82
+    - name: AnswerF1Score (Answer Extraction)
+      type: answer_f1_score__answer_extraction
+      value: 70.41
+    - name: AnswerExactMatch (Answer Extraction)
+      type: answer_exact_match_answer_extraction
+      value: 59.77
+---
+
+# Model Card of `lmqg/t5-large-squad-ae`
+This model is fine-tuned version of [t5-large](https://huggingface.co/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).
+
+
+### Overview
+- **Language model:** [t5-large](https://huggingface.co/t5-large)   
+- **Language:** en  
+- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_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/t5-large-squad-ae")
+
+# model prediction
+answers = model.generate_a("William Turner was an English painter who specialised in watercolour landscapes")
+
+```
+
+- With `transformers`
+```python
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", "lmqg/t5-large-squad-ae")
+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.")
+
+```
+
+## Evaluation
+
+
+- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/t5-large-squad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json) 
+
+|                  |   Score | Type    | Dataset                                                        |
+|:-----------------|--------:|:--------|:---------------------------------------------------------------|
+| AnswerExactMatch |   59.77 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| AnswerF1Score    |   70.41 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| BERTScore        |   91.91 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| Bleu_1           |   65.48 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| Bleu_2           |   62.11 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| Bleu_3           |   58.71 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| Bleu_4           |   55.66 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| METEOR           |   43.06 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| MoverScore       |   82.82 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+| ROUGE_L          |   69.67 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qg_squad
+ - dataset_name: default
+ - input_types: ['paragraph_sentence']
+ - output_types: ['answer']
+ - prefix_types: ['ae']
+ - model: t5-large
+ - max_length: 512
+ - max_length_output: 32
+ - epoch: 9
+ - batch: 4
+ - lr: 0.0001
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 32
+ - label_smoothing: 0.0
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-large-squad-ae/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",
+}
+
+```

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "lmqg_output/t5-large-squad-ae/best_model",
+  "_name_or_path": "lmqg_output/t5-large-squad-answer-extraction/model_dpyopu/epoch_8",
   "add_prefix": true,
   "architectures": [
     "T5ForConditionalGeneration"

eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json

@@ -0,0 +1 @@
+{"validation": {"Bleu_1": 0.6092786558604, "Bleu_2": 0.5752857781791739, "Bleu_3": 0.5409398353010405, "Bleu_4": 0.5088834730749616, "METEOR": 0.4044118256706168, "ROUGE_L": 0.6559758184242263, "BERTScore": 0.915732828428301, "MoverScore": 0.797161540973188, "AnswerF1Score": 66.48735591160741, "AnswerExactMatch": 53.074739829706715}, "test": {"Bleu_1": 0.6548460814625446, "Bleu_2": 0.6210863550939794, "Bleu_3": 0.5871105868832052, "Bleu_4": 0.5565669770193598, "METEOR": 0.43057968365185656, "ROUGE_L": 0.6967481728431596, "BERTScore": 0.9190753414804128, "MoverScore": 0.828223524086094, "AnswerF1Score": 70.40520439016043, "AnswerExactMatch": 59.77098593921024}}

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:358edcf5b640427e5555bc98194a407b281bd4b3f38563942bf907fee2596263
-size 2950730753
+oid sha256:2d264eafe36e89defd20628dad2d134fe4a0c25ea22199287b564064e663ae79
+size 2950737921

tokenizer_config.json

@@ -104,7 +104,7 @@
   "eos_token": "</s>",
   "extra_ids": 100,
   "model_max_length": 512,
-  "name_or_path": "lmqg_output/t5-large-squad-ae/best_model",
+  "name_or_path": "lmqg_output/t5-large-squad-answer-extraction/model_dpyopu/epoch_8",
   "pad_token": "<pad>",
   "special_tokens_map_file": null,
   "tokenizer_class": "T5Tokenizer",

trainer_config.json

@@ -0,0 +1 @@
+{"dataset_path": "lmqg/qg_squad", "dataset_name": "default", "input_types": ["paragraph_sentence"], "output_types": ["answer"], "prefix_types": ["ae"], "model": "t5-large", "max_length": 512, "max_length_output": 32, "epoch": 9, "batch": 4, "lr": 0.0001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 32, "label_smoothing": 0.0}