model update

README.md

@@ -14,7 +14,7 @@ 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."
+- 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/t5-base-tweetqa-qag
@@ -29,25 +29,25 @@ model-index:
     metrics:
     - name: BLEU4
       type: bleu4
-      value: 0.13263946554139405
+      value: 0.12931835496445465
     - name: ROUGE-L
       type: rouge-l
-      value: 0.36935780155247455
+      value: 0.36535644337488943
     - name: METEOR
       type: meteor
-      value: 0.3081166404528711
+      value: 0.30354623890919497
     - name: BERTScore
       type: bertscore
-      value: 0.9085398159959508
+      value: 0.9055079668504705
     - name: MoverScore
       type: moverscore
-      value: 0.6231917023300243
+      value: 0.6182219856624396
     - name: QAAlignedF1Score (BERTScore)
       type: qa_aligned_f1_score_bertscore
-      value: 0.9251122858041756
+      value: 0.9237100142709498
     - name: QAAlignedF1Score (MoverScore)
       type: qa_aligned_f1_score_moverscore
-      value: 0.6503876079996429
+      value: 0.646258531258488
 ---
 
 # Model Card of `lmqg/t5-base-tweetqa-qag`
@@ -89,7 +89,7 @@ from lmqg import TransformersQG
 # initialize model
 model = TransformersQG(language='en', model='lmqg/t5-base-tweetqa-qag')
 # model prediction
-question = model.generate_qa(list_context=["William Turner was an English painter who specialised in watercolour landscapes"], list_answer=["William Turner"])
+question = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
         
 ```
 
@@ -100,7 +100,7 @@ from transformers import pipeline
 # initialize model
 pipe = pipeline("text2text-generation", 'lmqg/t5-base-tweetqa-qag')
 # question generation
-question = pipe('generate question and answer:  Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.')
+question = pipe('generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.')
 
 ```
 
@@ -111,14 +111,14 @@ question = pipe('generate question and answer:  Beyonce further expanded her act
 
 | Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
 |:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
-| [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | default | 0.133 | 0.369 | 0.308 | 0.909 | 0.623 | [link](https://huggingface.co/lmqg/t5-base-tweetqa-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) | 
+| [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | default | 0.129 | 0.365 | 0.304 | 0.906 | 0.618 | [link](https://huggingface.co/lmqg/t5-base-tweetqa-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) | 
 
 
 ### Metrics (QAG)
 
 | Dataset | Type | QA Aligned F1 Score (BERTScore) | QA Aligned F1 Score (MoverScore) | Link |
 |:--------|:-----|--------------------------------:|---------------------------------:|-----:|
-| [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | default | 0.925 | 0.65 | [link](https://huggingface.co/lmqg/t5-base-tweetqa-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) | 
+| [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | default | 0.924 | 0.646 | [link](https://huggingface.co/lmqg/t5-base-tweetqa-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) | 
 
 
 
@@ -134,13 +134,13 @@ The following hyperparameters were used during fine-tuning:
  - model: t5-base
  - max_length: 256
  - max_length_output: 128
- - epoch: 14
+ - epoch: 15
  - batch: 32
  - lr: 0.0001
  - fp16: False
  - random_seed: 1
  - gradient_accumulation_steps: 2
- - label_smoothing: 0.0
+ - label_smoothing: 0.15
 
 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-base-tweetqa-qag/raw/main/trainer_config.json).
 

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "lmqg_output/t5_base_tweetqa/best_model",
+  "_name_or_path": "lmqg_output/t5_base_tweetqa/model_eszyci/epoch_10",
   "add_prefix": true,
   "architectures": [
     "T5ForConditionalGeneration"

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

@@ -1 +1 @@
-{"validation": {"Bleu_1": 0.3738463313336194, "Bleu_2": 0.25187710741309555, "Bleu_3": 0.17301367663064196, "Bleu_4": 0.12056853153991282, "METEOR": 0.3347661830653345, "ROUGE_L": 0.3823696390369866, "BERTScore": 0.9027718756456522, "MoverScore": 0.6215475528953677, "QAAlignedF1Score (BERTScore)": 0.9188373178327536, "QAAlignedF1Score (MoverScore)": 0.6489597272156573}, "test": {"Bleu_1": 0.4051769369789305, "Bleu_2": 0.2754558448773594, "Bleu_3": 0.18928799859905374, "Bleu_4": 0.13263946554139405, "METEOR": 0.3081166404528711, "ROUGE_L": 0.36935780155247455, "BERTScore": 0.9085398159959508, "MoverScore": 0.6231917023300243, "QAAlignedF1Score (BERTScore)": 0.9251122858041756, "QAAlignedF1Score (MoverScore)": 0.6503876079996429}}
+{"validation": {"Bleu_1": 0.3745080763582746, "Bleu_2": 0.2533067107107663, "Bleu_3": 0.17606641193871814, "Bleu_4": 0.12466416914978967, "METEOR": 0.33022954796005266, "ROUGE_L": 0.3766922696498115, "BERTScore": 0.9021096628280691, "MoverScore": 0.6197868517703407, "QAAlignedF1Score (BERTScore)": 0.919022094072905, "QAAlignedF1Score (MoverScore)": 0.6496686673949033}, "test": {"Bleu_1": 0.3929101495604297, "Bleu_2": 0.2668844135148667, "Bleu_3": 0.1839940844567295, "Bleu_4": 0.12931835496445465, "METEOR": 0.30354623890919497, "ROUGE_L": 0.36535644337488943, "BERTScore": 0.9055079668504705, "MoverScore": 0.6182219856624396, "QAAlignedF1Score (BERTScore)": 0.9237100142709498, "QAAlignedF1Score (MoverScore)": 0.646258531258488}}

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6de481d78e08319db5c05315e56fe506310cc08fb807168fc12425f511319949
-size 891614207
+oid sha256:494cd04d8952275505d4e477022569e499b9fb9d27d4f5c5c6cb7e707df4590d
+size 891617855

tokenizer_config.json

@@ -104,7 +104,7 @@
   "eos_token": "</s>",
   "extra_ids": 100,
   "model_max_length": 512,
-  "name_or_path": "lmqg_output/t5_base_tweetqa/best_model",
+  "name_or_path": "lmqg_output/t5_base_tweetqa/model_eszyci/epoch_10",
   "pad_token": "<pad>",
   "special_tokens_map_file": null,
   "tokenizer_class": "T5Tokenizer",

trainer_config.json

@@ -1 +1 @@
-{"dataset_path": "lmqg/qag_tweetqa", "dataset_name": "default", "input_types": ["paragraph"], "output_types": ["questions_answers"], "prefix_types": ["qag"], "model": "t5-base", "max_length": 256, "max_length_output": 128, "epoch": 14, "batch": 32, "lr": 0.0001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 2, "label_smoothing": 0.0}
+{"dataset_path": "lmqg/qag_tweetqa", "dataset_name": "default", "input_types": ["paragraph"], "output_types": ["questions_answers"], "prefix_types": ["qag"], "model": "t5-base", "max_length": 256, "max_length_output": 128, "epoch": 15, "batch": 32, "lr": 0.0001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 2, "label_smoothing": 0.15}