CODAIT · tedhtchang · Dec 30, 2019 · Jan 29, 2020 · Feb 12, 2020 · Feb 12, 2020
diff --git a/bert-text-classifier/README.md b/bert-text-classifier/README.md
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+# MAX for TensorFlow.js: Text Sentiment Classifier
+
+This is a TensorFlow.js port of the [MAX Text Sentiment Classifier](https://developer.ibm.com/exchanges/models/all/max-text-sentiment-classifier/) This model is able to detect whether a text fragment leans towards a positive or a negative sentiment.
+
+## Install
+
+### Browser
+
+```html
+<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
+<script src="https://cdn.jsdelivr.net/npm/@codait/text-sentiment-classifier"></script>
+```
+
+### Node.js
+
+```
+npm install --save @codait/text-sentiment-classifier
+```
+
+## Usage
+
+The complete examples for browser and Node.js environments are in the [`/examples`](https://github.com/CODAIT/max-tfjs-models/tree/master/text-sentiment-classifier/examples) directory.
+
+### Browser
+
+> **Note**: _When loaded in a browser, the global variable `textSentimentClassifier` will be available to access the API._
+
+```javascript
+
+textSentimentClassifier
+  .predict("i like strawberries")
+  .then(prediction => {
+    console.log(prediction)
+  });
+```
+
+### Node.js
+
+```javascript
+const tc = require('@codait/text-sentiment-classifier');
+tc.predict("i like strawberries").then(res=>console.log(res)); //{ pos: 0.9981953501701355, neg: 0.0018045296892523766 }
+
+```
+
+### API
+
+- **loadModel()**
+
+  Loads the model files.
+
+  Running in Node.js the first time will download the model assets locally under `/model` directory. The subsequent calls will load the model from the directory.
+
+  Returns the TensorFlow.js model.
+
+- **processInput(text)**
+
+  Processes the input text to the shape and format expected by the model.
+
+  `text` - sentence to be processed. It should be a sentence with a period although this is not necessary.
+
+  Returns a named tensor map that contains:
+  `{'segment_ids_1': Tensor of shape [128],
+    'input_ids_1': Tensor of shape [128],
+    'input_mask_1': Tensor of shape [128]}`
+
+- **runInference(inputFeatures)**
+
+  Runs inference on the named tensor map passed. The output is a tensor that contains softmax of positive and negative percentages.
+
+  `inputFeature` - a named tensor map representation of a text.
+
+  Returns the inference results as a 1D tensor.
+
+- **processOutput(tensor)**
+
+  Transform the inference output to a Json object.
+
+  `tensor` - the model output from running inference.
+
+  Returns an object containing: `{neg: number, pos: number}`
+
+
+- **predict(text)**
+
+  Loads the model, processes the input text, runs inference, processes the inference output, and returns a prediction object. This is a convenience function to avoid having to call each of the functions (`loadModel`, `processInput`, `runInference`, `processOutput`) individually.
+
+  `text` - sentence to be analyzed. It should be a sentence with a period although this is not necessary.
+
+  Returns an object containing: `{neg: number, pos: number}`
+
+- **encode(text)**
+
+  Tokenize the text as token ids using the BERT 32k vocabularies.
+
+  `text` - sentence to be encoded.
+
+  Returns an array of BERT token ids.
+
+- **idsToTokens(ids)**
+
+  Transform the BERT token ids into tokens.
+
+  `ids` - BERT token ids.
+
+  Returns an array of BERT tokens.
+
+- **version**
+
+  Returns the version
+
+## Model
+
+The model assets produced by converting the pre-trained model to the TensorFlow.js format can be found in the `/model` directory after loadModel is called in Node.js.
+
+## Resources
+
+- [MAX Text Sentiment Classifier](https://developer.ibm.com/exchanges/models/all/max-text-sentiment-classifier/)
+
+## License
+
+[Apache-2.0](https://github.com/CODAIT/max-tfjs-models/blob/master/LICENSE)
diff --git a/bert-text-classifier/examples/test.html b/bert-text-classifier/examples/test.html
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+<meta charset="utf-8"/>
+<html>
+  <head>
+  <title>text classifier</title>
+  </head>
+  <body onload=loadModel()>
+    <h1>Text Sentiment Classifier</h1>
+    <div id="div1">
+      <input type="text" id="text_input" name="text_input" value="Enter a sentence.">
+      <input type="button" id="submit_text" value="Analyze">
+    </div>
+    <p>
+      <label for="status">Status:</label>
+      <label name="status" id="status">Loading Model...</label>
+    </p>
+    <p>
+      <label for="status">Result:</label>
+      <label name="result" id="result"></label>
+    </p>
+  </body>
+  <script src="../dist/src/max.sentimentclass.js"></script>
+  <script>
+    let submitButton = document.getElementById("submit_text");
+    let statusElement = document.getElementById("status");
+    let resultElement = document.getElementById("result");
+    submitButton.addEventListener("click", runPredict);
+    function enableAnalyzeButton(){
+      submitButton.removeAttribute('disabled');
+    }
+
+    function disableAnalyzeButton(){
+      submitButton.setAttribute('disabled','true');
+    }
+
+    function loadModel(){
+      submitButton.setAttribute('disabled','true');
+      textSentimentClassifier.loadModel().then( () =>{
+        submitButton.removeAttribute('disabled');
+        updateStatus("Model Loaded");
+      })
+    }
+    function updateStatus(msg) {
+      statusElement.innerHTML = msg;
+    }
+    function appendResult(msg) {
+      resultElement.innerHTML += msg;
+    }
+    function runPredict(){
+      updateStatus("Running prediction...");
+      disableAnalyzeButton();
+      const text = document.getElementById("text_input").value;
+      setTimeout(() => { //yield the thread to update UI
+        textSentimentClassifier.predict(text)
+        .then((res) =>{
+          appendResult(`
+            <br> ${text}
+            <br>Positive &#128512;: ${res['pos'].toFixed(4)}
+            <br>Negative &#128534;: ${res['neg'].toFixed(4)}<br>`);
+          updateStatus("Ok");
+          enableAnalyzeButton();
+        })
+        .catch((err) => {
+          updateStatus(err);
+          enableAnalyzeButton();
+        })
+      }, 20);
+    }
+  </script>
+</html>
diff --git a/bert-text-classifier/examples/test.js b/bert-text-classifier/examples/test.js
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+const tc = require("../dist/src/max.sentimentclass.cjs.js");
+tc.predict("i like strawberries").then(res=>console.log(res));
+tc.encode("i like strawberries").then(res=>console.log(res));
diff --git a/bert-text-classifier/model/conversion_manifest.json b/bert-text-classifier/model/conversion_manifest.json
@@ -0,0 +1,124 @@
+{
+  "name": "Max-Text-Sentiment-Classifier",
+  "url": "https://developer.ibm.com/exchanges/models/all/max-text-sentiment-classifier/",
+  "source": "max-text-sentiment-classifier/1.0/assets.tar.gz/sentiment_BERT_base_uncased/",
+  "framework": "tf_js",
+  "converter": {
+    "tensorflowjs": {
+      "version": "1.2.9",
+      "params": {
+        "output_node_names": "loss/Softmax",
+        "input_format": "tf_saved_model",
+        "output_json": true
+      }
+    }
+  },
+  "output": [
+    "model.json",
+	"group1-shard1of105.bin",
+	"group1-shard2of105.bin",
+	"group1-shard3of105.bin",
+	"group1-shard4of105.bin",
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+  ]
+}