asahi417 commited on
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model update

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  1. README.md +6 -6
README.md CHANGED
@@ -21,7 +21,7 @@ widget:
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  - text: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records <hl> ."
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  example_title: "Question Generation Example 3"
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  model-index:
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- - name: lmqg/t5-large-subjqa-vanilla-electronics
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  results:
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  - task:
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  name: Text2text Generation
@@ -48,7 +48,7 @@ model-index:
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  value: 50.46
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  ---
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- # Model Card of `lmqg/t5-large-subjqa-vanilla-electronics`
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  This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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@@ -66,7 +66,7 @@ This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large)
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  from lmqg import TransformersQG
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  # initialize model
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- model = TransformersQG(language="en", model="lmqg/t5-large-subjqa-vanilla-electronics")
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  # model prediction
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  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
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  ```python
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  from transformers import pipeline
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- pipe = pipeline("text2text-generation", "lmqg/t5-large-subjqa-vanilla-electronics")
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  output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
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  ```
@@ -85,7 +85,7 @@ output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting
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  ## Evaluation
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- - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-large-subjqa-vanilla-electronics/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json)
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  | | Score | Type | Dataset |
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  |:-----------|--------:|:------------|:-----------------------------------------------------------------|
@@ -119,7 +119,7 @@ The following hyperparameters were used during fine-tuning:
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  - gradient_accumulation_steps: 8
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  - label_smoothing: 0.15
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- The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-large-subjqa-vanilla-electronics/raw/main/trainer_config.json).
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  ## Citation
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  ```
 
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  - text: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records <hl> ."
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  example_title: "Question Generation Example 3"
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  model-index:
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+ - name: research-backup/t5-large-subjqa-vanilla-electronics-qg
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  results:
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  - task:
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  name: Text2text Generation
 
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  value: 50.46
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  ---
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+ # Model Card of `research-backup/t5-large-subjqa-vanilla-electronics-qg`
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  This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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  from lmqg import TransformersQG
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  # initialize model
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+ model = TransformersQG(language="en", model="research-backup/t5-large-subjqa-vanilla-electronics-qg")
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  # model prediction
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  questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
 
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  ```python
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  from transformers import pipeline
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+ pipe = pipeline("text2text-generation", "research-backup/t5-large-subjqa-vanilla-electronics-qg")
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  output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
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  ```
 
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  ## Evaluation
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+ - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-large-subjqa-vanilla-electronics-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json)
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  | | Score | Type | Dataset |
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  |:-----------|--------:|:------------|:-----------------------------------------------------------------|
 
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  - gradient_accumulation_steps: 8
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  - label_smoothing: 0.15
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+ The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/t5-large-subjqa-vanilla-electronics-qg/raw/main/trainer_config.json).
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  ## Citation
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  ```