docs_src/notebooks/predict_demo.ipynb

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   "source": [
    "# Prediction Functions Demo\n",
    "This Jupyter notebook demonstrates the usage of the prediction functions that are part of ChemREL."
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    ":::{tip}\n",
    "To run a copy of this notebook yourself, download the corresponding `predict_demo.ipynb` file [here](https://github.com/PEESEgroup/ChemREL/blob/main/docs_src/notebooks/predict_demo.ipynb).\n",
    ":::"
   ],
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   "cell_type": "markdown",
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   "source": [
    "## Import ChemREL\n",
    "To begin, import the `predict` submodule from the `functions` package in ChemREL, as follows."
   ]
  },
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     "start_time": "2023-06-13T23:04:48.569354Z"
    }
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   "source": [
    "from chemrel.functions import predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict Submodule\n",
    "The `predict` submodule contains two available functions, `predict_span()` and `predict_rel()`. Their usage is demonstrated below. For the full documentation of each function, see the [ChemREL Functions Reference](https://peesegroup.github.io/ChemREL/functions_index.html)."
   ]
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   "source": [
    "We first define the text string to predict as a variable, `example_text`."
   ]
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   "source": [
    "example_text = '''Two physical properties of the fluorinated carboxylic acid,\n",
    "2H-hexadecafluoro-2-decenoic acid (C10F16H2O2), were investigated in this study: \n",
    "melting point and solubility. Melting point data were ascertained on a traditional melting point\n",
    "apparatus and a differential scanning calorimeter (DSC), while solubility data were obtained using an LC−MS/MS system.\n",
    "A melting point of 105 ± 1 °C and an aqueous solubility of 64 ± 5 ng/μL at ambient temperatures were observed'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Span Categorization\n",
    "We demonstrate how `predict_span()` can be used to predict spans, as follows. This assumes that the span categorization model provided by ChemREL has been initialized at `chemrelmodels/sc/tok2vec`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   },
   "outputs": [],
   "source": [
    "spans_dict = predict.predict_span(\"chemrelmodels/sc/tok2vec\", example_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now print the contents of `spans_dict` to view the predicted spans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CHEMICAL': [('C10F16H2O2', 0.9998276), ('fluorinated carboxylic acid', 0.99959916), ('2H-hexadecafluoro-2-decenoic acid', 0.9974232)], 'MP': [('105 ± 1 °C', 0.99998033)]}\n"
     ]
    }
   ],
   "source": [
    "print(spans_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relation Prediction\n",
    "We demonstrate how `predict_rel()` can be used to predict relations, as follows. This assumes that the span categorization and relation extraction models provided by ChemREL have been initialized at `chemrelmodels/sc/tok2vec` and `chemrelmodels/rel/tok2vec`, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-13T23:10:11.372988Z",
     "start_time": "2023-06-13T23:10:11.368041Z"
    }
   },
   "outputs": [],
   "source": [
    "rels_dict = predict.predict_rel(\"chemrelmodels/sc/tok2vec\", \"chemrelmodels/rel/tok2vec\", example_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now print the contents of `rels_dict` to view the predicted relations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{('CHEMICAL', 'MP'): [(('fluorinated carboxylic acid', '105 ± 1 °C'), 0.999992), (('2H-hexadecafluoro-2-decenoic acid', '105 ± 1 °C'), 0.9999999), (('C10F16H2O2', '105 ± 1 °C'), 0.9999999)]}\n"
     ]
    }
   ],
   "source": [
    "print(rels_dict)"
   ]
  }
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