asahi417 commited on
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model update

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  1. README.md +7 -7
README.md CHANGED
@@ -21,7 +21,7 @@ widget:
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  - text: "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/bart-base-subjqa-movies
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  results:
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  - task:
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  name: Text2text Generation
@@ -48,9 +48,9 @@ model-index:
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  value: 62.91
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  ---
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- # Model Card of `lmqg/bart-base-subjqa-movies`
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  This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: movies) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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- This model is continuously fine-tuned with [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad).
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  ### Overview
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  - **Language model:** [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad)
@@ -66,7 +66,7 @@ This model is continuously fine-tuned with [lmqg/bart-base-squad](https://huggin
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  from lmqg import TransformersQG
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  # initialize model
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- model = TransformersQG(language="en", model="lmqg/bart-base-subjqa-movies")
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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/bart-base-subjqa-movies")
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  output = pipe("<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("<hl> Beyonce <hl> further expanded her acting career, starring as
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  ## Evaluation
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- - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-base-subjqa-movies/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.movies.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: 2
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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/bart-base-subjqa-movies/raw/main/trainer_config.json).
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  ## Citation
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  ```
 
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  - text: "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/bart-base-subjqa-movies-qg
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  results:
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  - task:
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  name: Text2text Generation
 
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  value: 62.91
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  ---
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+ # Model Card of `lmqg/bart-base-subjqa-movies-qg`
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  This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: movies) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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+
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  ### Overview
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  - **Language model:** [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad)
 
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  from lmqg import TransformersQG
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  # initialize model
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+ model = TransformersQG(language="en", model="lmqg/bart-base-subjqa-movies-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", "lmqg/bart-base-subjqa-movies-qg")
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  output = pipe("<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/lmqg/bart-base-subjqa-movies-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.movies.json)
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  | | Score | Type | Dataset |
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  |:-----------|--------:|:-------|:-----------------------------------------------------------------|
 
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  - gradient_accumulation_steps: 2
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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/bart-base-subjqa-movies-qg/raw/main/trainer_config.json).
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  ## Citation
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  ```