model update

README.md

@@ -21,7 +21,7 @@ widget:
 - text: "como <hl> el gobierno de Abbott <hl> que asumió el cargo el 18 de septiembre de 2013."
   example_title: "Question Generation Example 3" 
 model-index:
-- name: lmqg/mt5-small-esquad
+- name: lmqg/mt5-small-esquad-qg
   results:
   - task:
       name: Text2text Generation
@@ -66,7 +66,7 @@ model-index:
       value: 63.75
 ---
 
-# Model Card of `lmqg/mt5-small-esquad`
+# Model Card of `lmqg/mt5-small-esquad-qg`
 This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
 
 
@@ -84,7 +84,7 @@ This model is fine-tuned version of [google/mt5-small](https://huggingface.co/go
 from lmqg import TransformersQG
 
 # initialize model
-model = TransformersQG(language="es", model="lmqg/mt5-small-esquad")
+model = TransformersQG(language="es", model="lmqg/mt5-small-esquad-qg")
 
 # model prediction
 questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre")
@@ -95,7 +95,7 @@ questions = model.generate_q(list_context="a noviembre , que es también la esta
 ```python
 from transformers import pipeline
 
-pipe = pipeline("text2text-generation", "lmqg/mt5-small-esquad")
+pipe = pipeline("text2text-generation", "lmqg/mt5-small-esquad-qg")
 output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.")
 
 ```
@@ -103,7 +103,7 @@ output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la In
 ## Evaluation
 
 
-- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) 
+- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) 
 
 |            |   Score | Type    | Dataset                                                          |
 |:-----------|--------:|:--------|:-----------------------------------------------------------------|
@@ -117,7 +117,7 @@ output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la In
 | ROUGE_L    |   24.62 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
 
 
-- ***Metric (Question & Answer Generation)***: QAG metrics are computed with *the gold answer* and generated question on it for this model, as the model cannot provide an answer. [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.json)
+- ***Metric (Question & Answer Generation)***: QAG metrics are computed with *the gold answer* and generated question on it for this model, as the model cannot provide an answer. [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.json)
 
 |                                 |   Score | Type    | Dataset                                                          |
 |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
@@ -149,7 +149,7 @@ The following hyperparameters were used during fine-tuning:
  - gradient_accumulation_steps: 1
  - label_smoothing: 0.15
 
-The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-esquad/raw/main/trainer_config.json).
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-esquad-qg/raw/main/trainer_config.json).
 
 ## Citation
 ```