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Daniel Dale
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added tensorboard.dev link for latest model training/eval viz
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README.md

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@@ -87,7 +87,7 @@ The entire initial Deep Classiflie system (raw dataset, model, analytics modules
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**Model Training**
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- Fine-tune a base model (currently HuggingFace's [ALBERT implementation](https://huggingface.co/transformers/model_doc/albert.html) with some minor customizations) in tandem with a simple embedding reflecting the semantic shift associated with the medium via which the statement was conveyed (i.e., for the POC, just learn the tweet vs non-tweet transformation) (using [Pytorch](https://pytorch.org/))
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- Explore the latest model's training session on tensorboard.dev.
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- Explore the latest model's training session on [tensorboard.dev](https://tensorboard.dev/experiment/rGNQpYnYSOaHb2A84xRAzw).
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- N.B. neuro-symbolic methods<sup id="a6">[6](#f6)</sup> that leverage knowledge bases and integrate symbolic reasoning with connectionist methods are not used in this model. Use of these approaches may be explored in [future research](#further-research) using this framework.
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**Analysis & Reporting**

docs/_layouts/default.html

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<!DOCTYPE html>
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<html lang="{{ site.lang | default: "en-US" }}">
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<head>
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{% if site.google_analytics %}
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<script async src="https://www.googletagmanager.com/gtag/js?id=UA-26562413-2"></script>
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<script>
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window.dataLayer = window.dataLayer || [];
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function gtag(){dataLayer.push(arguments);}
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gtag('js', new Date());
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gtag('config', 'UA-26562413-2');
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</script>
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{% endif %}
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<meta name="theme-color" content="#157878" />
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Bokeh.set_log_level("info");
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</script>
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{% endif %}
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{% if site.google_analytics %}
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<script async src="https://www.googletagmanager.com/gtag/js?id=UA-26562413-2"></script>
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<script>
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window.dataLayer = window.dataLayer || [];
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function gtag(){dataLayer.push(arguments);}
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gtag('js', new Date());
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gtag('config', 'UA-26562413-2');
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</script>
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{% endif %}
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</head>
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<body>
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<section class="page-header">

docs/about.md

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**Model Training**
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- Fine-tune a base model (currently HuggingFace's [ALBERT implementation](https://huggingface.co/transformers/model_doc/albert.html) with some minor customizations) in tandem with a simple embedding reflecting the semantic shift associated with the medium via which the statement was conveyed (i.e., for the POC, just learn the tweet vs non-tweet transformation) (using [Pytorch](https://pytorch.org/))
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- Explore the latest model's training session on tensorboard.dev.
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- Explore the latest model's training session on [tensorboard.dev](https://tensorboard.dev/experiment/rGNQpYnYSOaHb2A84xRAzw).
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- N.B. neuro-symbolic methods<sup id="a6">[6](#f6)</sup> that leverage knowledge bases and integrate symbolic reasoning with connectionist methods are not used in this model. Use of these approaches may be explored in [future research](#further-research) using this framework.
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**Analysis & Reporting**

docs/perf_explorer.html

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<div class="outer_container">
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<div class="perf_exp_temporal">
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<div class="box cmatrix_title">Temporal Confusion Matrices</div>
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<!--TODO: Add title div for temporal confusion matrices-->
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{% include bokeh_viz/temporal_tabs_tags.html %}
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</div>
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<div class="perf_exp_conf">
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<div class="box cmatrix_title">Confusion Matrices by Percentile Confidence Bucket<sup id="a0" class="small"><a href="#f0">[0]</a></sup></div>
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<!--TODO: Add title div for conf confusion matrices-->
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{% include bokeh_viz/conf_tabs_tags.html %}
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</div>
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</div>

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