diff --git a/README.md b/README.md index 8ee711c162..445f0dcdef 100644 --- a/README.md +++ b/README.md @@ -140,7 +140,7 @@ The following code will log one record into a dataset called `example-dataset`: import rubrix as rb rb.log( - rb.TextClassificationRecord(inputs="My first Rubrix example"), + rb.TextClassificationRecord(text="My first Rubrix example"), name='example-dataset' ) ``` @@ -210,7 +210,7 @@ for item in dataset: records.append( rb.TextClassificationRecord( - inputs=item["text"], + text=item["text"], prediction=list(zip(prediction['labels'], prediction['scores'])) ) ) @@ -237,7 +237,7 @@ rb_df = rb_df[rb_df.status == "Validated"] # select text input and the annotated label train_df = pd.DataFrame({ - "text": rb_df.inputs.transform(lambda r: r["text"]), + "text": rb_df.text, "label": rb_df.annotation, }) ``` diff --git a/docs/getting_started/concepts.rst b/docs/getting_started/concepts.rst index f9738fafa2..bbabb46404 100644 --- a/docs/getting_started/concepts.rst +++ b/docs/getting_started/concepts.rst @@ -22,13 +22,13 @@ Let's take a look at Rubrix's components and methods: Dataset ^^^^^^^ -A dataset is a collection of records stored in Rubrix. The main things you can do with a ``Dataset`` are to ``log`` records and to ``load`` the records of a ``Dataset`` into a ``Pandas.Dataframe`` from a Python app, script, or a Jupyter/Colab notebook. +A dataset is a collection of records stored in Rubrix. The main things you can do with a ``Dataset`` are to ``log`` records and to ``load`` the records of a ``Dataset`` into a ``Pandas.Dataframe`` from a Python app, script, or a Jupyter/Colab notebook. Record ^^^^^^ -A record is a data item composed of ``inputs`` and, optionally, ``predictions`` and ``annotations``. Usually, inputs are the information your model receives (for example: 'Macbeth'). +A record is a data item composed of ``text`` inputs and, optionally, ``predictions`` and ``annotations``. Think of predictions as the classification that your system made over that input (for example: 'Virginia Woolf'), and think of annotations as the ground truth that you manually assign to that input (because you know that, in this case, it would be 'William Shakespeare'). Records are defined by the type of ``Task``\ they are related to. Let's see three different examples: @@ -42,10 +42,9 @@ Let's see examples of a spam classifier. .. code-block:: python record = rb.TextClassificationRecord( - inputs={ - "text": "Access this link to get free discounts!" - }, - prediction = [('SPAM', 0.8), ('HAM', 0.2)] + text="Access this link to get free discounts!", + + prediction = [('SPAM', 0.8), ('HAM', 0.2)], prediction_agent = "link or reference to agent", annotation = "SPAM", @@ -54,7 +53,6 @@ Let's see examples of a spam classifier. metadata={ # Information about this record "split": "train" }, - ) Multi-label text classification record @@ -65,9 +63,8 @@ Another similar task to Text Classification, but yet a bit different, is Multi-l .. code-block:: python record = rb.TextClassificationRecord( - inputs={ - "text": "I can't wait to travel to Egypts and visit the pyramids" - }, + text="I can't wait to travel to Egypts and visit the pyramids", + multi_label = True, prediction = [('travel', 0.8), ('history', 0.6), ('economy', 0.3), ('sports', 0.2)], @@ -127,7 +124,7 @@ A prediction is a piece information assigned to a record, a label or a set of la Metadata ^^^^^^^^ -Metada will hold extra information that you want your record to have: if it belongs to the training or the test dataset, a quick fact about something regarding that specific record... Feel free to use it as you need! +Metada will hold extra information that you want your record to have: if it belongs to the training or the test dataset, a quick fact about something regarding that specific record... Feel free to use it as you need! Methods ------- diff --git a/docs/getting_started/setup&installation.rst b/docs/getting_started/setup&installation.rst index 852057b084..791383d4ba 100644 --- a/docs/getting_started/setup&installation.rst +++ b/docs/getting_started/setup&installation.rst @@ -65,7 +65,7 @@ The following code will log one record into a data set called ``example-dataset` import rubrix as rb rb.log( - rb.TextClassificationRecord(inputs="My first Rubrix example"), + rb.TextClassificationRecord(text="My first Rubrix example"), name='example-dataset' ) diff --git a/docs/guides/cookbook.ipynb b/docs/guides/cookbook.ipynb index 075f290291..14cbd0c17b 100644 --- a/docs/guides/cookbook.ipynb +++ b/docs/guides/cookbook.ipynb @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -197,7 +197,7 @@ "4 6.291863e+17 If I make a game as a #windows10 Universal App..." ] }, - "execution_count": 17, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -257,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -265,18 +265,10 @@ "import rubrix as rb\n", "\n", "# load rubrix dataset\n", - "df = rb.load('zeroshot_example')\n", - "\n", - "# inputs can be dicts to support multifield classifiers, we just use the text here. \n", - "df['text'] = df.inputs.transform(lambda r: r['text'])\n", - "\n", - "# we create a dict for turning our annotations (labels) into numeric ids\n", - "label2id = {label: id for id, label in enumerate(df.annotation.unique())}\n", + "dataset_rb = rb.load('zeroshot_example', as_pandas=False)\n", "\n", - "\n", - "# create 🤗 dataset from pandas with labels as numeric ids\n", - "dataset = Dataset.from_pandas(df[['text', 'annotation']])\n", - "dataset = dataset.map(lambda example: {'labels': label2id[example['annotation']]})" + "# create 🤗 dataset with text and labels as numeric ids\n", + "train_ds = dataset_rb.prepare_for_training() " ] }, { @@ -296,7 +288,7 @@ "def tokenize_function(examples):\n", " return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\n", "\n", - "train_dataset = dataset.map(tokenize_function, batched=True).shuffle(seed=42)\n", + "train_dataset = train_ds.map(tokenize_function, batched=True).shuffle(seed=42)\n", "\n", "trainer = Trainer(model=model, train_dataset=train_dataset)\n", "\n", @@ -736,10 +728,8 @@ "train_dataset = rb.load(\"tweet_eval_emojis\", limit=limit_num)\n", "\n", "# 2. Pre-processing training pandas dataframe\n", - "ready_input = [row['text'] for row in train_dataset.inputs]\n", - "\n", "train_df = pd.DataFrame()\n", - "train_df['text'] = ready_input\n", + "train_df['text'] = train_dataset['text']\n", "train_df['label'] = train_dataset['annotation']\n", "\n", "# 3. Save as csv with tab delimiter\n", @@ -845,8 +835,8 @@ "labels = [\"happy\", \"sad\"]\n", "\n", "# Create a sentence\n", - "input_text = \"I am so glad you liked it!\"\n", - "sentence = Sentence(input_text)\n", + "text = \"I am so glad you liked it!\"\n", + "sentence = Sentence(text)\n", "\n", "# Predict for these labels\n", "tars.predict_zero_shot(sentence, labels)\n", @@ -857,7 +847,7 @@ "\n", "# Building a TextClassificationRecord\n", "record = rb.TextClassificationRecord(\n", - " inputs=input_text,\n", + " text=text,\n", " prediction=prediction,\n", " prediction_agent=\"tars-base\",\n", ")\n", @@ -885,13 +875,13 @@ "from flair.models import TextClassifier\n", "from flair.data import Sentence\n", "\n", - "input_text = \"Du erzählst immer Quatsch.\" \n", + "text = \"Du erzählst immer Quatsch.\" \n", "\n", "# Load our pre-trained classifier\n", "classifier = TextClassifier.load(\"de-offensive-language\")\n", "\n", "# Creating Sentence object\n", - "sentence = Sentence(input_text)\n", + "sentence = Sentence(text)\n", "\n", "# Make the prediction\n", "classifier.predict(sentence, return_probabilities_for_all_classes=True)\n", @@ -901,7 +891,7 @@ "\n", "# Building a TextClassificationRecord\n", "record = rb.TextClassificationRecord(\n", - " inputs=input_text,\n", + " text=text,\n", " prediction=prediction,\n", " prediction_agent=\"de-offensive-language\",\n", ")\n", @@ -994,7 +984,7 @@ "import rubrix as rb\n", "import stanza\n", "\n", - "input_text = (\n", + "text = (\n", " \"There are so many NLP libraries available, I don't know which one to choose!\"\n", ")\n", "\n", @@ -1005,7 +995,7 @@ "nlp = stanza.Pipeline(lang=\"en\", processors=\"tokenize,sentiment\")\n", "\n", "# Analizing the input text\n", - "doc = nlp(input_text)\n", + "doc = nlp(text)\n", "\n", "# This model returns 0 for negative, 1 for neutral and 2 for positive outcome.\n", "# We are going to log them into Rubrix using a dictionary to translate numbers to labels.\n", @@ -1026,7 +1016,7 @@ "\n", "# Building a TextClassificationRecord\n", "record = rb.TextClassificationRecord(\n", - " inputs=input_text,\n", + " text=text,\n", " prediction=entities,\n", " prediction_agent=\"stanza/en\",\n", ")\n", @@ -1150,7 +1140,7 @@ "hash": "b709380ea7d1cb2eb4650c0f11ac7e002ec6a534602815725771481b4784238c" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1164,9 +1154,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/guides/datasets.ipynb b/docs/guides/datasets.ipynb index bfe414f18e..d164f49288 100644 --- a/docs/guides/datasets.ipynb +++ b/docs/guides/datasets.ipynb @@ -42,7 +42,7 @@ " print(record)\n", " \n", "# Index into the dataset\n", - "dataset_rb[0] = rb.TextClassificationRecord(inputs=\"replace record\")\n", + "dataset_rb[0] = rb.TextClassificationRecord(text=\"replace record\")\n", "\n", "# log a dataset to the Rubrix web app\n", "rb.log(dataset_rb, \"my_dataset\")" @@ -154,7 +154,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -168,7 +168,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/docs/guides/metrics.ipynb b/docs/guides/metrics.ipynb index 6822b7f055..f5e0152e6b 100644 --- a/docs/guides/metrics.ipynb +++ b/docs/guides/metrics.ipynb @@ -1206,7 +1206,7 @@ } } }, - "image/png": 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9c+aFy67dnh+LeHnJdGnZgrA3J2qNbSKAAAIIIIAAAggggAACCCCAAAKhLkDYa9fDhL12fpfUNA46spew17JgaI4AAggggAACCCCAAAIIIIAAAggg4FGAsNeuOAh77fwIe7P4MbLXsqBojgACCCCAAAIIIIAAAggggAACCFzCAoS9dp1P2GvnR9hL2GtZQTRHAAEEEEAAAQQQQAABBBBAAAEEEHAECHvtaoGw186PsJew17KCaI4AAggggAACCCCAAAIIIIAAAgggQNgbnBog7LV05AFtmQEzTuNw5IjnB7llbBUXl27ZCzRHAAEEEEAAAQQQQAABBBBAAAEEEAgFAUb22vUiYa+dHyN7s/hlDHsTEsLkvQ/DvQpfVTpd6t2bZtkLNEcAAQQQQAABBBBAAAEEEEAAAQQQCAUBwl67XiTstfMj7PUR9k6eGiEul2fkO6qmEfZa1iDNEUAAAQQQQAABBBBAAAEEEEAAgVARIOy160nCXjs/wl7CXssKojkCCCCAAAIIIIAAAggggAACCCCAgCNA2GtXC4S9dn6EvYS9lhVEcwQQQAABBBBAAAEEEEAAAQQQQAABwt7g1ABhr6UjD2jLDJh1zl6mcbAsMJojgAACCCCAAAIIIIAAAggggAACl5AAI3vtOpuw186Pkb1Z/Ah7LQuK5ggggAACCCCAAAIIIIAAAggggMAlLEDYa9f5hL12foS9hL2WFURzBBBAAAEEEEAAAQQQQAABBBBAAAFHgLDXrhYIe+38CHsJey0riOYIIIAAAggggAACCCCAAAIIIIAAAoS9wakBwl5LR+bszQzINA6WBUVzBBBAAAEEEEAAAQQQQAABBBBA4BIWYGSvXecT9tr5MbI3ix9hr2VB0RwBBBBAAAEEEEAAAQQQQAABBBC4hAUIe+06n7DXzo+wl7DXsoJojgACCCCAAAIIIIAAAggggAACCCDgCBD22tUCYa+dH2EvYa9lBdEcAQQQQAABBBBAAAEEEEAAAQQQQICwNzg1QNhr6cicvZkBmcbBsqBojgACCCCAAAIIIIAAAggggAACCFzCAozstet8wl47P0b2ZvEj7LUsKJojgAACCCCAAAIIIIAAAggggAACl7AAYa9d5xP22vkR9hL2WlYQzRFAAAEEEEAAAQQQQAABBBBAAAEEHAHCXrtaIOy18yPsJey1rCCaI4AAAggggAACCCCAAAIIIIAAAggQ9ganBgh7LR2ZszczoM00Dn/vCvOrN/Q9WBBAAAEEEEAAAQQQQAABBBBAAAEEQk+Akb12fUrYa+fHyN4sfjZh7zffhsm7H0R47ZG2rVxSrhxhr2XZ0hwBBBBAAAEEEEAAAQQQQAABBBC4IAUIe+26hbDXzo+wl7DXsoJojgACCCCAAAIIIIAAAggggAACCCDgCBD22tUCYa+dH2EvYa9lBdEcAQQQQAABBBBAAAEEEEAAAQQQQICwNzg1QNhr6cicvZkBmcbBsqBojgACCCCAAAIIIIAAAggggAACCFzCAozstet8wl47P0b2ZvEj7LUsKJojgAACCCCAAAIIIIAAAggggAACl7AAYa9d5xP22vkR9hL2WlYQzRFAAAEEEEAAAQQQQAABBBBAAAEEHAHCXrtaIOy18yPsJey1rCCaI4AAAggggAACCCCAAAIIIIAAAggQ9ganBgh7LR2ZszczINM4WBYUzRFAAAEEEEAAAQQQQAABBBBAAIFLWICRvXadT9hr58fI3ix+hL2WBUVzBBBAAAEEEEAAAQQQQAABBBBA4BIWIOy163zCXjs/wl7CXssKojkCCCCAAAIIIIAAAggggAACCCCAgCNA2GtXC4S9dn6Evecp7E1LF0k8EOZH76VLkSJ+rMYqCCCAAAIIIIAAAggggAACCCCAAALnXYCw164LCHvt/Ah7z2PYO39BhPzxp5fAN0ykY9tUwl7LGqc5AggggAACCCCAAAIIIIAAAgggcK4ECHvtpAl77fwIe89z2Pv9j57D3thYwl7L8qY5AggggAACCCCAAAIIIIAAAgggcE4FCHvtuAl77fwIewl7LSuI5ggggAACCCCAAAIIIIAAAggggAACjgBhr10tEPba+RH2EvZaVhDNEUAAAQQQQAABBBBAAAEEEEAAAQQIe4NTA4S9lo67DiTL+g3hsnxVuNctdevkkpIl0yUlRWTeggjZ8Zvn6QcKF0qXNq1cUrCgyP79ItPfipSkJM+bL39DurRo5pKwMJFffgmTmXMivO5LsyYuufmmdLPOR8vDZdNmz/seESHSo4tLihZNl8OHw2TOvHDZtdvzvl9eMl1atkiTuLh0SUgIk8lTI8Tl8rw7d1RNk3r3ppkVvvk2TN79wPu+t23lknLl0kUf0KZz9jKNg2UB0xwBBBBAAAEEEEAAAQQQQAABBBC4gAQY2WvXGYS9dn6M7M3id6GGvYkHRX780Xsgr4dy261pkju3ZVHQHAEEEEAAAQQQQAABBBBAAAEEEEDgrAQIe8+Kzd2IsNfOj7D3Igp7Z86OkMREz6OSry6bLi2auwh7LT8TNEcAAQQQQAABBBBAAAEEEEAAAQTOVoCw92zlTrUj7LXzI+wl7LWsIJojgAACCCCAAAIIIIAAAggggAACCDgChL12tUDY66ffkaRkSU1NlYL54zK1YM7ezIAX8jQOjOz1s9hZDQEEEEAAAQQQQAABBBBAAAEEEDhPAoS9dvCEvT78jiWnSPzwqbJ6/VdmzYrly8mE4b2kSKH85u+EvaEZ9u7a5Xm6h4xHrA/dY0EAAQQQQAABBBBAAAEEEEAAAQQQCI4AYa+dI2GvD7/pc5bK/CVrZdaEQRITk1u6x4+RsleWkGH9OxD2ZmMXKiN7/94VJq9Ni/BaHfXrpkn1amlmnU8/C5c0H7nvtVenS4kS6ZKSLPLTz77D5NJXpEvBgoF/wFNS/GsTHe3feqyFAAIIIIAAAggggAACCCCAAAIInCsBwl47acJeH37Nuzwr9WpVkU4tG5o1l6/dIv2GTJJta96UsLAwRvZm8buUw96Vq8O9VlP3zi532PvOwgj59TfPgW+RwunSuqXLhL3794fJT9t9h8N3VD8VPG//OUw2bvK+L7f9K00qVDiVTu/d53vb4WEiRYteWKOY01wih4/43nc9xgIFLqx9tztt0xoBBBBAAAEEEEAAAQQQQACB0BUg7LXrW8JeH35VGnST4fEd5d6alc2a32//QzQA3rh4kuSLiyXsJew1AjqyNyfD3ukzIiTpmOdivbF8urRo5nKHvW/P9T4q+eGmLnfYu/TjcNm8xXM4HBkpokH1hRj2zlsYIQcSvX+IH2nukiJFAj9R/v13mLhOkXpdSpcmSPZlxOsIIIAAAggggAACCCCAAAII+CtA2OuvVPbrEfZ68UtPT5cKd7eXySP7Ss2qFc2aO37fJY3bDZRV80ZLieKF5URqmixenip/7vTeEXfflS4Vb8gtB/5JlVnzT43A9LY8cF+YlC2dS3769YR8tNLX2iJd20ZI7qhw2fzVSdn8pff1K9yQLnffESWutDSZvSBVDvkYHVmvtkj5a6Jk5+6TsmCR72Cr+QNhUuqyXPL9z8dl+WrvIy8L5k+XRx/KJRHhYbL6sxPy3x+9r1+1ksjt/8olycfTZOpM30lcg7oi15WNkh1/npRFH/ve9zYtwqVQ/kj55ofjsuZT7/ty5RUije6NFN3qhx+nys5d3t1r10iXm68PoAYahEnZK/yvgW7tIiQqV7hs/PKkbDk1xbTH5eYb06VmtShxudJk1oJUOXLU+7HWqyNS/uooOXg4VY4m+XaMyxsmBeIi5bedJ+TIUR/1G6Z9FCkR4SI//ZrqM2CNzp0uV18ZJcdS0uT1WX7UwL0i15WJkp17T0riQd/7fmWpCMkXGyFbvz8un23y7lLuKpG6NSMlPV1kd0Kqzw9qZIRIyeJRcvRYqhxL9r0vUVFhUjAuUvbsPyHHT/jcvBQvEiG5IsNlx58nJNXH7kREiJQrHSWprjRJOub7nBQeHiYF80VKUopL0n3NWSIiun6e6Ag5ekz7yPex5soVLrlzhcvRZN+OKpE3JtKA+LN+RESYxERFBLTvMbkjJMnPfdFzr7r7sy/OvqempUnKcd/u+pmOigyX5OMucfnhHohLoI7h4SJ5ckea696Jk773PToqXCIjwk29+7OYYw2wBo6luCQ5xfe+aA3oOSnlRJqpeV+LHqcebyB96m89Xtw1ECZ5ckf4XwO5wyUyPLDPRqCO2pvH/Pisai1qTfpdAzGRoj+DXkg1cOy4S9LO83lA/7+m50e/zwPnogbSRI4d932e+V8NuCTV5fu6FBsTKfq/gBytAVeaqUlfi3Mt0ONM8716QNfIQK8FF3MN6LVa/6+RfMIlLj9qICevqWFhIrHRkeaa5E8N6H7r/l+MNaDnDK2bQD5LJ1PT5Lgfnw2tX/2s6v/F/TkPXJA1kJomKX78v8qpgaTjqZJ+gZwHjp9ME+0rX8u5+H+VnhsDqoEL4P/WznnA1PslUAN6vP5+v4qKOvUdiOXsBQh7fdjpyN4RAzpJ3RqVzJpZR/aePT0tEUAAAQQQQAABBBBAAAEEEEAAAQQQQACB4AkQ9vqw1Ckb6teqIh09zNkbvK5gSwgggAACCCCAAAIIIIAAAggggAACCCCAwNkLEPb6sJs2Z6ksWLJWZk0YJHlicku3+DFS9soSMqx/h7NXpyUCCCCAAAIIIIAAAggggAACCCCAAAIIIBBkAcJeH6BJx1Kk/7Apsm7TN2bNCteXkYnDe0uxIgWC3BVsDgEEAhFISTkhiYeOyGVFC5n5YbMuOq9hwoGDUqRQfonQCWpZEEDgghQ4kpQsqampUjB/3AW5f+wUApeSgP6/98jRY1KsSMFM11auqZdSFXCsF7rAnn2JZ3xGnX3mmnqh9x77F6oC+ryntLQ0j9879ycekrx5YiQ6OuoMghMnU+XgoSNSrHABCdOJbVkQCIIAYa+fiIePJIl+CDU4YkEAgfMr8MSgCbJ6/amn0BUqmE+a1L9T+nV92L1T+uOM/kijX1p1GfJkO2neqNb53WneHYFLWEA/kz0GjM30wNNjySkSP3yq+7NcsXw5mTC8F9fZS7hOOPTzJ6Cf0ZcmzZXf/9pjduL9N4bLtWVLmT9zTT1//cI7I5BRYObCFTL7vZVy8qRLTqamSpP77pJ+XZqbVbimUisInF+BxSs3yNipC2X1gjGZduTPv/dK9wFj3dfXhxrWkGf6tpXIyAjRgPjVmYvklTffd3+vfWVEb9H/E7MgYCtA2GsrSHsEEDjnAnpBvLdmZSl9eTHZ/NX30uOpcfLOlGfkphvKio74rdG0l/Rs30RaNb1H1mzYKr0HT5Tlc0dJqRJFz/m+8oYIXOoC23f8JY89McL8+DJ5ZF+pWbWiIZk+Z6nMPz1NUkxMbunONEmXeqlw/OdJYN3GreY62rnV/fJA/TulYP68Eh0VZUYfcU09T53C2yKQReC/23+Xh7sMkRnjBkjlW66X3/7cLfe3eUrmTB5sgiGuqZQMAudHQMPczv1flp27EqR40UJnhL1d+o+WvLHR8vyAzrI7IVFadB0ig/u2kUZ1q8vWbb9Iq57DZdbEgXLT9WVl4hvvyZJVm2TVvNHZ3rl6fo6Qd71YBQh7L9aeY78RQMAtULt5P3nkgbuly2ONzAgkHUH49cppEpUr0qzTsPUAadnkHhP+siCAwLkTSDjwj7ToNsyMPBo29i0Z9Ux3d9irD0CtV6uKdOIBqOeuQ3gnBLII6Kiipp2ekevLXSEvDOxyhg/XVEoGgQtD4POvf5D2fV+Uj2e/KKUvL2526q4mveQ/PR4xoRHX1Aujn9iLS0/A5XKJTtGw+rOv5fU5SzOFvXp3eLVGj8vsV56WWypcbXBGjH9bdCqWiSN6yZjX5ssPP/8hr7/c37y2b/8/cnezPrLw9aFywzVXXnqYHHFQBQh7g8rJxhBA4FwL/LFzrzR4LN49YnDB4rUyY/4yWTprpHtXdNqHMqUvyzTVw7neT94PgUtNQEcEtu3zgtx1+81mpH2VBt0yhb369+HxHc0ofV2+3/6H+bK6cfEkyRcXe6lxcbwInBeBxH+OyF0PPiG177jV3Baut4JXvbW8dHi0gUTnjhKuqeelW3hTBM4Q0OkEOz35kvz4y5/yRIem5m6Z5eu2yMzxT0lc3jzmGss1lcJB4PwJfLx6s4yaMi9T2Lvj913SuN1AWfvuOCla+NQzn2YtXCGLVqyXBVOHmmkHC+SPk0G9H3Pv+I212mW6E+78HRHvfLELEPZe7D3I/iNwCQvof3T19vC42Bh5c9wAiQgPN7exLVv7ubmAOoteSGNjY8zcvSwIIJDzAvowp/7PTTFvNGpwd3MrWsawV0cTVri7fab/zDr/IdZb10oUL5zzO8k7IICAGVHUrPOzZl77OypXkENHkszcvQ3qVDXXTK6pFAkCF47AtDlLTUgUE51btv34m7kzplfHphIeHs419cLpJvbkEhXILux1pmnIOJBBf0SdMnORCYV1iofrr74i04Ak/f+yXn/1OsyCgI0AYa+NHm0RQOC8CeiowV6DJ8qehAMyc8JAKZAvr9kXRiGdty7hjRFwCzi3oTW7v6bE5okx//7W/GVSq/ot8kC9O8xoXv3P7IgBnaRujUrmdUb2UkAInHsBJ+z99IOJUqhAnNmB9z76VEa+Mls2L50iC5es426Zc98tvCMCZwh8uvlb6RY/RjYtmWxG8m7Ysk36PPuKPNmthbRofDfXVGoGgf9v786jvCrPO4A/OEIVTcI2IwRUBOUAFZdYl+ZgGpVEKElYGguOBhGsgiwGUaGyCISmChpWQQkqRoIaJCjRHKwF9IAYqArFutTG2AiigCyGRVxm2nNvMpMMQuCQucDc+dx/OMzc+773+bwv53f8en/PPcwCf+7J3ud+Pqn8BcR7Ptlbt84X4taBnuw9zMuXy+mFvblcVkURyLfA9h27YsDwyfHR7o/j3nGDy4PepOqy/oKrn5kZNf/Qs/fS4pujx3cv1bM339tCdUeQQPJV8NnznqlwR5Nmzkv7CnZsd0Ha2iFp2dD+6+dFbz17j6CVcyvVTaCsn+DD00fEGa1+//bvny1YEqN/9GC8sviBWLpyTdoH32dqddsZ6j3SBCb++LFY/PzLsWDWD8tvrd+tE+O42sfEuOF9fKYeaQvmfqqdwN7C3r317B078aHYsGlrec/eN369NmaMH5x66dlb7bZNpgULezPlNTgBApUtkAS83fqMjpKS0pgwql/aniE5khYODYvqpQHw37S/Lob0L44rulwSS5avjhtGTImnHx4fTRoVVvbtGI8AgQMU2LNnb/J11LlPPhsPTR4WtY/9q/SJpWYnN4oxN/c6wBGdRoBAZQgk//aS1isTx/SLzVt/FzeNmR6NiurHxDH9faZWBrAxCFSCwC8Xr0j7e95zx43R9rw2se69TdG++Ja4uW/36NmtffhMrQRkQxA4CIGkNdlnn5WkbQQnzHgsnp4zLm1fVlBQkI72TzeNT99F8S9Dron3Nm2JbteNihGDeqQPQJS1eZg9dVi0adksJs18LJ5atCKSlmbJGA4Cf4mAsPcv0XMtAQKHXGDDB1vj4u8O+ty89ep+MZbOn5z+fMnzq6L/sEnl5wz//vfi8s6XHPJ7NSEBAn8U2DPsTXpuJ//hmjyNnxyntzwlpoy9IYoa/P4FFg4CBA6NQBIaDbptatpKJTnO/0qr9EnBBvW+5DP10CyBWQjsVyD5HzIzZv8i5i9cGlu3bY/jj6udtkXq17NzHH10QfrCNp+p+2V0AoFKF/j1/74bnXoOqzDud7751fjXW69Nf/b22vejz5C7Yt36TenfO7dvm/bkTb6BmgTFbRh5EQAADLVJREFUUx+YH/f8ZEH6u+RJ/RnjboqzTj+10u/TgNVPQNhb/dZcxQSqhUBJaWm8v3FLFNWvU97OoVoUrkgCVUwg+Ypb8pbxsmCpit2+2yWQG4Hk66NJaFTWu/dPC/OZmptlVkgOBNZv2BwNC+vt9ck/n6k5WGAl5FIgeWDp+NrHpoHunsfujz+JLdu27/PfdS5BFJW5gLA3c2ITECBAgAABAgQIECBAgAABAgQIECBAIHsBYW/2xmYgQIAAAQIECBAgQIAAAQIECBAgQIBA5gLC3syJTUCAAAECBAgQIECAAAECBAgQIECAAIHsBYS92RubgQABAgQIECBAgAABAgQIECBAgAABApkLCHszJzYBAQIECBAgQIAAAQIECBAgQIAAAQIEshcQ9mZvbAYCBAgQIECAAAECBAgQIECAAAECBAhkLiDszZzYBAQIECBAgAABAgQIECBAgAABAgQIEMheQNibvbEZCBAgQIAAAQIECBAgQIAAAQIECBAgkLmAsDdzYhMQIECAAAECBAgQIECAAAECBAgQIEAgewFhb/bGZiBAgAABAgQIECBAgAABAgQIECBAgEDmAsLezIlNQIAAAQIECBAgQIAAAQIECBAgQIAAgewFhL3ZG5uBAAECBAgQIECAAAECBAgQIECAAAECmQsIezMnNgEBAgQIECBAgAABAgQIECBAgAABAgSyFxD2Zm9sBgIECBAgQIAAAQIECBAgQIAAAQIECGQuIOzNnNgEBAgQIECAAAECBAgQIECAAAECBAgQyF5A2Ju9sRkIECBAgAABAgQOUGDde5ti+45d0eq0k2PN62/FCYX14oQGdQ/w6s+fNmLcfdG0ScPoXdzxoMdwIQECBAgQIECAAIGqIiDsrSor5T4JECBAgAABAtVA4I67H46iBnXi6m4d4tLim2Pi6P5p8Huwx2XX3hZtWjaLkTdedbBDuI4AAQIECBAgQIBAlREQ9laZpXKjBAgQIECAAIH8C3yn560xdkjvaFhYP77VY2i88Iu7o6Cg4KALF/YeNJ0LCRAgQIAAAQIEqqCAsLcKLppbJkCAAAECBAjkSeDN36yLl195Mz799LO4feqcGNK/ON5ZtyEWP78qrr3yW9GkUWG0Pa/NPkvesfOjmPbgE/HcC6tj0+Zt0bpF07iy6zei3dfOiSTsPanxCdG4UWE8+cwLUbNmQVze6ZIo7touatU8Oh1z1qMLY+6Tz6bXJseZrZtH/15d0z+T49EFS2Llqtfj+qs6x5z5/x5v/XZ9DOzdNR1z0sx58auXXosdO3dFi2YnRrdOF8W3v/HVPC2PWggQIECAAAECBKqQgLC3Ci2WWyVAgAABAgQI5FEgCVJ/uXhFvPnW2vhw+8644CutY8ny1XFS46JoemLDaHnqSdG908V7Lb2ktDSKr/9B/Ncbb6fntGnVLJauWBM7d+2Oe+64MQ17X3vzt3H26afFN79+bqx9d2Ma2N47bnB5gDzl/p9Haen/xWnNmkRJSUnMnvdMvP3Oe7F47oQ4/rhj40cz5sZ9c55K5z/njBZpD+F/7HRRTPrxvFi/YXMa/NaqVTNeXP1GvL9xS0y7fVAel0lNBAgQIECAAAECVUBA2FsFFsktEiBAgAABAgSqg0DSr7dRUb3ocdml0fF7Q+POkX3326930bKXY+DwyTF+ZN/4+4vPL2fa+MG2tPdvEvY2blgYE0b3ixo1aqS/T1pFnH926xh2w5UVWJOgd+uHO+I/Vr8RN42ZHg9PHxFntGqehr2PPL4oZk8ZFi2an5hek4TMZ1zcK4q7tKswzu7dn8Qxx9SqDsulRgIECBAgQIAAgSNQQNh7BC6KWyJAgAABAgQIVEeBLr1HxNhbekXDovrR4YpbDqhf7/QHn4ipD8yPZU9Mibpf+sLn2PbWs/f6oRPS88qewP3vt9bGndMfieUvvlrh+lkTh8a5Z7VMw96nn10ZT88ZX+H3g0dPi4VLVqZPDV9wTuv4uwvOTJ8sdhAgQIAAAQIECBA4XALC3sMlb14CBAgQIECAAIF45fXfRPe+Y/YpkbRxeOqh2/f5+wkz5sbMOU/FSwtn7PWJ2r2FvQOGTU7bNSRh7++274y//Xa/tD/vgN5do9nJX05/1vnq4bG/sDcZY/7CZfHc8v+MFateS1tHXFPcMQZde5mVJUCAAAECBAgQIHBYBIS9h4XdpAQIECBAgAABAolA8lK2LR9ujyXPr4pFS1+KsUOviTunPRLNT2kcXTpcGEfVqBGF9evsE+vxhcti2O0zK/TgTU5O2iwUHHVU2sahTctmMfLGq8rH+NOwd9nKV+K6W+6Kn04dHmedfmp6zjvvbogOVwzZf9j7hznK6hg5/v5Y8G/LY83i+9O5HQQIECBAgAABAgQOtYCw91CLm48AAQIECBAgQOBzAkm/3iTU7dW9Q9qvd/yIvtG6xcn7lUqewu3Y45/ji8fXjt6Xd4yz25wWv3rx1Vj16v/EuOF99hv2btm2PS7sPCA6tW8b3TtdFBs3bYt7Zy9IX+r2557sTea9/PofRL+ru0SblqfEjp0fxai7ZkVpaWn87N5R5f2B91uAEwgQIECAAAECBAhUooCwtxIxDUWAAAECBAgQIHBwAkm/3lGDe0aTRoXxtS4DY82i+6KgoOCABkuC2dvuvD8NaMuOwX26pcFxtz6j469bNK3wZO8NI6bEZyUlcfcPv5+ePuvRhTHtwcfTNgzJ0bl920ieGJ41aWice2bLSFpFLNyjZ2/yIrYBwydV6PPb7sJzYmDvf4jmTb98QPftJAIECBAgQIAAAQKVLSDsrWxR4xEgQIAAAQIECBwWge07P4odO3ZFUYM6BxwUl93ox598Gus3bI5GhfX22vt3XwUl1236YFsUFdaNWjWPPix1m5QAAQIECBAgQIBAmYCw114gQIAAAQIECBAgQIAAAQIECBAgQIBADgSEvTlYRCUQIECAAAECBAgQIECAAAECBAgQIEBA2GsPECBAgAABAgQIECBAgAABAgQIECBAIAcCwt4cLKISCBAgQIAAAQIECBAgQIAAAQIECBAgIOy1BwgQIECAAAECBAgQIECAAAECBAgQIJADAWFvDhZRCQQIECBAgAABAgQIECBAgAABAgQIEBD22gMECBAgQIAAAQIECBAgQIAAAQIECBDIgYCwNweLqAQCBAgQIECAAAECBAgQIECAAAECBAgIe+0BAgQIECBAgAABAgQIECBAgAABAgQI5EBA2JuDRVQCAQIECBAgQIAAAQIECBAgQIAAAQIEhL32AAECBAgQIECAAAECBAgQIECAAAECBHIgIOzNwSIqgQABAgQIECBAgAABAgQIECBAgAABAsJee4AAAQIECBAgQIAAAQIECBAgQIAAAQI5EBD25mARlUCAAAECBAgQIECAAAECBAgQIECAAAFhrz1AgAABAgQIECBAgAABAgQIECBAgACBHAgIe3OwiEogQIAAAQIECBAgQIAAAQIECBAgQICAsNceIECAAAECBAgQIECAAAECBAgQIECAQA4EhL05WEQlECBAgAABAgQIECBAgAABAgQIECBAQNhrDxAgQIAAAQIECBAgQIAAAQIECBAgQCAHAsLeHCyiEggQIECAAAECBAgQIECAAAECBAgQICDstQcIECBAgAABAgQIECBAgAABAgQIECCQAwFhbw4WUQkECBAgQIAAAQIECBAgQIAAAQIECBAQ9toDBAgQIECAAAECBAgQIECAAAECBAgQyIGAsDcHi6gEAgQIECBAgAABAgQIECBAgAABAgQICHvtAQIECBAgQIAAAQIECBAgQIAAAQIECORAQNibg0VUAgECBAgQIECAAAECBAgQIECAAAECBIS99gABAgQIECBAgAABAgQIECBAgAABAgRyICDszcEiKoEAAQIECBAgQIAAAQIECBAgQIAAAQLCXnuAAAECBAgQIECAAAECBAgQIECAAAECORAQ9uZgEZVAgAABAgQIECBAgAABAgQIECBAgAABYa89QIAAAQIECBAgQIAAAQIECBAgQIAAgRwICHtzsIhKIECAAAECBAgQIECAAAECBAgQIECAgLDXHiBAgAABAgQIECBAgAABAgQIECBAgEAOBIS9OVhEJRAgQIAAAQIECBAgQIAAAQIECBAgQEDYaw8QIECAAAECBAgQIECAAAECBAgQIEAgBwLC3hwsohIIECBAgAABAgQIECBAgAABAgQIECAg7LUHCBAgQIAAAQIECBAgQIAAAQIECBAgkAMBYW8OFlEJBAgQIECAAAECBAgQIECAAAECBAgQEPbaAwQIECBAgAABAgQIECBAgAABAgQIEMiBwP8DNifViR7MMjgAAAAASUVORK5CYII=", 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", 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