model update

README.md

@@ -17,7 +17,7 @@ widget:
 - text: "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-np
+- name: research-backup/t5-base-tweetqa-qag-np
   results:
   - task:
       name: Text2text Generation
@@ -62,7 +62,7 @@ model-index:
       value: 65.68
 ---
 
-# Model Card of `lmqg/t5-base-tweetqa-qag-np`
+# Model Card of `research-backup/t5-base-tweetqa-qag-np`
 This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question & answer pair generation task on the [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
 This model is fine-tuned without a task prefix.
 
@@ -80,7 +80,7 @@ This model is fine-tuned without a task prefix.
 from lmqg import TransformersQG
 
 # initialize model
-model = TransformersQG(language="en", model="lmqg/t5-base-tweetqa-qag-np")
+model = TransformersQG(language="en", model="research-backup/t5-base-tweetqa-qag-np")
 
 # model prediction
 question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
@@ -91,7 +91,7 @@ question_answer_pairs = model.generate_qa("William Turner was an English painter
 ```python
 from transformers import pipeline
 
-pipe = pipeline("text2text-generation", "lmqg/t5-base-tweetqa-qag-np")
+pipe = pipeline("text2text-generation", "research-backup/t5-base-tweetqa-qag-np")
 output = pipe("Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
 
 ```
@@ -99,7 +99,7 @@ output = pipe("Beyonce further expanded her acting career, starring as blues sin
 ## Evaluation
 
 
-- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-base-tweetqa-qag-np/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) 
+- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-base-tweetqa-qag-np/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) 
 
 |                                 |   Score | Type    | Dataset                                                              |
 |:--------------------------------|--------:|:--------|:---------------------------------------------------------------------|
@@ -139,7 +139,7 @@ The following hyperparameters were used during fine-tuning:
  - gradient_accumulation_steps: 2
  - label_smoothing: 0.0
 
-The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-base-tweetqa-qag-np/raw/main/trainer_config.json).
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/t5-base-tweetqa-qag-np/raw/main/trainer_config.json).
 
 ## Citation
 ```