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5
examples/scibert/deploy/deploy.ipynb
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@ -182,9 +182,8 @@
" positive_likelihood = torch.nn.Softmax(dim=1)(result.logits)[0][1]\n",
" tokens = tensors[\"input_ids\"][0]\n",
"\n",
" attentions = np.sum(result.attentions[-1].numpy()[0], axis=0)[0][\n",
" 1:-1\n",
" ] # Tuple of `torch.FloatTensor` (one for each layer) of shape\n",
" attentions = np.sum(result.attentions[-1].numpy()[0], axis=0)[0][1:-1]\n",
" # Tuple of `torch.FloatTensor` (one for each layer) of shape\n",
" # `(batch_size, num_heads, sequence_length, sequence_length)`.\n",
"\n",
" explanation = []\n",

12
examples/scibert/deploy/index.html
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@ -2238,9 +2238,8 @@ def get_tokenizer_and_model(
<span class="n">positive_likelihood</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Softmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)(</span><span class="n">result</span><span class="o">.</span><span class="n">logits</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
<span class="n">tokens</span> <span class="o">=</span> <span class="n">tensors</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="n">attentions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">attentions</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">numpy</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span>
<span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span>
<span class="p">]</span> <span class="c1"># Tuple of `torch.FloatTensor` (one for each layer) of shape</span>
<span class="n">attentions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">attentions</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">numpy</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># Tuple of `torch.FloatTensor` (one for each layer) of shape</span>
<span class="c1"># `(batch_size, num_heads, sequence_length, sequence_length)`.</span>
<span class="n">explanation</span> <span class="o">=</span> <span class="p">[]</span>
@ -2302,9 +2301,8 @@ def find_highlights(sentence: str) -> EvaluatedSentence:
positive_likelihood = torch.nn.Softmax(dim=1)(result.logits)[0][1]
tokens = tensors["input_ids"][0]
attentions = np.sum(result.attentions[-1].numpy()[0], axis=0)[0][
1:-1
] # Tuple of `torch.FloatTensor` (one for each layer) of shape
attentions = np.sum(result.attentions[-1].numpy()[0], axis=0)[0][1:-1]
# Tuple of `torch.FloatTensor` (one for each layer) of shape
# `(batch_size, num_heads, sequence_length, sequence_length)`.
explanation = []
@ -2480,7 +2478,7 @@ To check out the Dockerimage, go to <a href="/examples/scibert/additional-files"
<small>
Last update:
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 17, 2022</span>
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 29, 2022</span>
</small>

6
examples/scibert/train/index.html
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@ -2057,7 +2057,7 @@ If you're only here for <code>great-ai</code>, feel free to skip the next cell.<
<span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">({</span><span class="s2">&quot;text&quot;</span><span class="p">:</span> <span class="n">X</span><span class="p">,</span> <span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="n">y</span><span class="p">})</span>
<span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="s2">&quot;text&quot;</span><span class="p">],</span> <span class="n">truncation</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> <span class="n">batched</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="o">.</span><span class="n">remove_columns</span><span class="p">(</span><span class="s2">&quot;text&quot;</span><span class="p">)</span>
<span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span><span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span><span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># test is actually validation</span>
<span class="p">)</span>
<span class="n">f1_score</span> <span class="o">=</span> <span class="n">load_metric</span><span class="p">(</span><span class="s2">&quot;f1&quot;</span><span class="p">)</span>
@ -2121,7 +2121,7 @@ dataset = (
Dataset.from_dict({"text": X, "label": y})
.map(lambda v: tokenizer(v["text"], truncation=True), batched=True)
.remove_columns("text")
.train_test_split(test_size=0.2, shuffle=True)
.train_test_split(test_size=0.2, shuffle=True) # test is actually validation
)
f1_score = load_metric("f1")
@ -2430,7 +2430,7 @@ Model weights saved in pretrained/pytorch_model.bin
<small>
Last update:
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 17, 2022</span>
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 29, 2022</span>
</small>

2
examples/scibert/train/train.ipynb
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@ -246,7 +246,7 @@
" Dataset.from_dict({\"text\": X, \"label\": y})\n",
" .map(lambda v: tokenizer(v[\"text\"], truncation=True), batched=True)\n",
" .remove_columns(\"text\")\n",
" .train_test_split(test_size=0.2, shuffle=True)\n",
" .train_test_split(test_size=0.2, shuffle=True) # test is actually validation\n",
")\n",
"\n",
"f1_score = load_metric(\"f1\")\n",

4
how-to-guides/install/index.html
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@ -897,7 +897,7 @@
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>pip install great-ai
</code></pre></div>
<blockquote>
<p>Python 3.8 or later is required.</p>
<p>Python 3.7 or later is required.</p>
</blockquote>
<p>This will work on all major operating systems.</p>
<h2 id="command-line-tools">Command-line tools<a class="headerlink" href="#command-line-tools" title="Permanent link">#</a></h2>
@ -926,7 +926,7 @@
<small>
Last update:
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 15, 2022</span>
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 29, 2022</span>
</small>

2
how-to-guides/install/install.md
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@ -5,7 +5,7 @@ Provided you already have [Python3](https://www.python.org/downloads/){ target=_
```sh
pip install great-ai
```
> Python 3.8 or later is required.
> Python 3.7 or later is required.
This will work on all major operating systems.

3
index.html
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@ -979,6 +979,7 @@
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> Docker support for deployment</li>
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> Deployable Jupyter Notebooks</li>
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> Dashboard for online monitoring and analysing traces</li>
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> Active support for Python 3.7, 3.8, 3.9, and 3.10</li>
</ul>
<h2 id="roadmap">Roadmap<a class="headerlink" href="#roadmap" title="Permanent link">#</a></h2>
<ul class="task-list">
@ -1051,7 +1052,7 @@
<small>
Last update:
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 24, 2022</span>
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 29, 2022</span>
</small>

1
index.md
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@ -41,6 +41,7 @@ Applying AI is becoming increasingly easier but many case studies have shown tha
- [x] Docker support for deployment
- [x] Deployable Jupyter Notebooks
- [x] Dashboard for online monitoring and analysing traces
- [x] Active support for Python 3.7, 3.8, 3.9, and 3.10
## Roadmap

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