model update

README.md

@@ -21,7 +21,7 @@ widget:
 - text: "generate question: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records . <hl>"
   example_title: "Question Generation Example 3" 
 model-index:
-- name: lmqg/t5-small-squad-no-answer
+- name: research-backup/t5-small-squad-qg-no-answer
   results:
   - task:
       name: Text2text Generation
@@ -48,7 +48,7 @@ model-index:
       value: 62.07
 ---
 
-# Model Card of `lmqg/t5-small-squad-no-answer`
+# Model Card of `research-backup/t5-small-squad-qg-no-answer`
 This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
 This model is fine-tuned without answer information, i.e. generate a question only given a paragraph (note that normal model is fine-tuned to generate a question given a pargraph and an associated answer in the paragraph).
 
@@ -66,7 +66,7 @@ This model is fine-tuned without answer information, i.e. generate a question on
 from lmqg import TransformersQG
 
 # initialize model
-model = TransformersQG(language="en", model="lmqg/t5-small-squad-no-answer")
+model = TransformersQG(language="en", model="research-backup/t5-small-squad-qg-no-answer")
 
 # model prediction
 questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
@@ -77,7 +77,7 @@ questions = model.generate_q(list_context="William Turner was an English painter
 ```python
 from transformers import pipeline
 
-pipe = pipeline("text2text-generation", "lmqg/t5-small-squad-no-answer")
+pipe = pipeline("text2text-generation", "research-backup/t5-small-squad-qg-no-answer")
 output = pipe("generate question: <hl>  Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>")
 
 ```
@@ -85,7 +85,7 @@ output = pipe("generate question: <hl>  Beyonce further expanded her acting care
 ## Evaluation
 
 
-- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-squad-no-answer/raw/main/eval/metric.first.sentence.paragraph_sentence.question.lmqg_qg_squad.default.json) 
+- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-squad-qg-no-answer/raw/main/eval/metric.first.sentence.paragraph_sentence.question.lmqg_qg_squad.default.json) 
 
 |            |   Score | Type    | Dataset                                                        |
 |:-----------|--------:|:--------|:---------------------------------------------------------------|
@@ -119,7 +119,7 @@ The following hyperparameters were used during fine-tuning:
  - gradient_accumulation_steps: 4
  - label_smoothing: 0.15
 
-The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-small-squad-no-answer/raw/main/trainer_config.json).
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/t5-small-squad-qg-no-answer/raw/main/trainer_config.json).
 
 ## Citation
 ```