diff --git a/examples/chainer_sentiment.py b/examples/chainer_sentiment.py
new file mode 100644
index 000000000..84075a231
--- /dev/null
+++ b/examples/chainer_sentiment.py
@@ -0,0 +1,307 @@
+'''WIP --- Doesn't work well yet'''
+import plac
+import random
+
+import pathlib
+import cPickle as pickle
+from itertools import izip
+
+import spacy
+
+import cytoolz
+import numpy as np
+import chainer.links as L
+import chainer.functions as F
+from chainer import Chain, Variable, report
+import chainer.training
+import chainer.optimizers
+from chainer.training import extensions
+from chainer.iterators import SerialIterator
+from chainer.datasets.tuple_dataset import TupleDataset
+
+
+class SentimentAnalyser(object):
+    @classmethod
+    def load(cls, path, nlp, max_length=100):
+        raise NotImplementedError
+        #with (path / 'config.json').open() as file_:
+        #    model = model_from_json(file_.read())
+        #with (path / 'model').open('rb') as file_:
+        #    lstm_weights = pickle.load(file_)
+        #embeddings = get_embeddings(nlp.vocab)
+        #model.set_weights([embeddings] + lstm_weights)
+        #return cls(model, max_length=max_length)
+
+    def __init__(self, model, max_length=100):
+        self._model = model
+        self.max_length = max_length
+
+    def __call__(self, doc):
+        X = get_features([doc], self.max_length)
+        y = self._model.predict(X)
+        self.set_sentiment(doc, y)
+
+    def pipe(self, docs, batch_size=1000, n_threads=2):
+        for minibatch in cytoolz.partition_all(batch_size, docs):
+            minibatch = list(minibatch)
+            sentences = []
+            for doc in minibatch:
+                sentences.extend(doc.sents)
+            Xs = get_features(sentences, self.max_length)
+            ys = self._model.predict(Xs)
+            for sent, label in zip(sentences, ys):
+                sent.doc.sentiment += label - 0.5
+            for doc in minibatch:
+                yield doc
+
+    def set_sentiment(self, doc, y):
+        doc.sentiment = float(y[0])
+        # Sentiment has a native slot for a single float.
+        # For arbitrary data storage, there's:
+        # doc.user_data['my_data'] = y
+
+class Classifier(Chain):
+    def __init__(self, predictor):
+        super(Classifier, self).__init__(predictor=predictor)
+
+    def __call__(self, x, t):
+        y = self.predictor(x)
+        loss = F.softmax_cross_entropy(y, t)
+        accuracy = F.accuracy(y, t)
+        report({'loss': loss, 'accuracy': accuracy}, self)
+        return loss
+
+
+class SentimentModel(Chain):
+    def __init__(self, shape, **settings):
+        Chain.__init__(self,
+            embed=_Embed(shape['nr_vector'], shape['nr_dim'], shape['nr_hidden']),
+            encode=_Encode(shape['nr_hidden'], shape['nr_hidden']),
+            attend=_Attend(shape['nr_hidden'], shape['nr_hidden']),
+            predict=_Predict(shape['nr_hidden'], shape['nr_class']))
+
+    def __call__(self, sentence):
+        return self.predict(
+                  self.attend(
+                      self.encode(
+                          self.embed(sentence))))
+
+
+class _Embed(Chain):
+    def __init__(self, nr_vector, nr_dim, nr_out):
+        Chain.__init__(self,
+            embed=L.EmbedID(nr_vector, nr_dim),
+            project=L.Linear(None, nr_out, nobias=True))
+        #self.embed.unchain_backward()
+
+    def __call__(self, sentence):
+        return [self.project(self.embed(ts)) for ts in F.transpose(sentence)]
+
+
+class _Encode(Chain):
+    def __init__(self, nr_in, nr_out):
+        Chain.__init__(self,
+            fwd=L.LSTM(nr_in, nr_out),
+            bwd=L.LSTM(nr_in, nr_out),
+            mix=L.Bilinear(nr_out, nr_out, nr_out))
+
+    def __call__(self, sentence):
+        self.fwd.reset_state()
+        fwds = map(self.fwd, sentence)
+        self.bwd.reset_state()
+        bwds = reversed(map(self.bwd, reversed(sentence)))
+        return [F.elu(self.mix(f, b)) for f, b in zip(fwds, bwds)]
+
+
+class _Attend(Chain):
+    def __init__(self, nr_in, nr_out):
+        Chain.__init__(self)
+
+    def __call__(self, sentence):
+        sent = sum(sentence)
+        return sent
+
+
+class _Predict(Chain):
+    def __init__(self, nr_in, nr_out):
+        Chain.__init__(self,
+            l1=L.Linear(nr_in, nr_in),
+            l2=L.Linear(nr_in, nr_out))
+
+    def __call__(self, vector):
+        vector = self.l1(vector)
+        vector = F.elu(vector)
+        vector = self.l2(vector)
+        return vector
+
+
+class SentenceDataset(TupleDataset):
+    def __init__(self, nlp, texts, labels, max_length):
+        self.max_length = max_length
+        sents, labels = self._get_labelled_sentences(
+            nlp.pipe(texts, batch_size=5000, n_threads=3),
+            labels)
+        TupleDataset.__init__(self,
+            get_features(sents, max_length),
+            labels)
+
+    def _get_labelled_sentences(self, docs, doc_labels):
+        labels = []
+        sentences = []
+        for doc, y in izip(docs, doc_labels):
+            for sent in doc.sents:
+                sentences.append(sent)
+                labels.append(y)
+        return sentences, labels
+
+
+class DocDataset(TupleDataset):
+    def __init__(self, nlp, texts, labels):
+        self.max_length = max_length
+        TupleDataset.__init__(self,
+            get_features(
+                nlp.pipe(texts, batch_size=5000, n_threads=3), self.max_length),
+            labels)
+
+
+
+def read_data(data_dir, limit=0):
+    examples = []
+    for subdir, label in (('pos', 1), ('neg', 0)):
+        for filename in (data_dir / subdir).iterdir():
+            with filename.open() as file_:
+                text = file_.read()
+            examples.append((text, label))
+    random.shuffle(examples)
+    if limit >= 1:
+        examples = examples[:limit]
+    return zip(*examples) # Unzips into two lists
+
+
+def get_features(docs, max_length):
+    docs = list(docs)
+    Xs = np.zeros((len(docs), max_length), dtype='int32')
+    for i, doc in enumerate(docs):
+        j = 0
+        for token in doc:
+            if token.has_vector and not token.is_punct and not token.is_space:
+                Xs[i, j] = token.norm
+                j += 1
+                if j >= max_length:
+                    break
+    return Xs
+
+
+def get_embeddings(vocab, max_rank=1000):
+    if max_rank is None:
+        max_rank = max(lex.rank+1 for lex in vocab if lex.has_vector)
+    vectors = np.ndarray((max_rank+1, vocab.vectors_length), dtype='float32')
+    for lex in vocab:
+        if lex.has_vector and lex.rank < max_rank:
+            lex.norm = lex.rank+1
+            vectors[lex.rank + 1] = lex.vector
+        else:
+            lex.norm = 0
+    return vectors
+
+
+def train(train_texts, train_labels, dev_texts, dev_labels,
+        lstm_shape, lstm_settings, lstm_optimizer, batch_size=100, nb_epoch=5,
+        by_sentence=True):
+    print("Loading spaCy")
+    nlp = spacy.load('en', entity=False)
+    for lex in nlp.vocab:
+        if lex.rank >= (lstm_shape['nr_vector'] - 1):
+            lex.norm = 0
+        else:
+            lex.norm = lex.rank+1
+    #print("Get embeddings")
+    #embeddings = get_embeddings(nlp.vocab)
+    print("Make model")
+    model = Classifier(SentimentModel(lstm_shape, **lstm_settings))
+    print("Parsing texts...")
+    if by_sentence:
+        train_data = SentenceDataset(nlp, train_texts, train_labels, lstm_shape['max_length'])
+        dev_data = SentenceDataset(nlp, dev_texts, dev_labels, lstm_shape['max_length'])
+    else:
+        train_data = DocDataset(nlp, train_texts, train_labels)
+        dev_data = DocDataset(nlp, dev_texts, dev_labels)
+    train_iter = SerialIterator(train_data, batch_size=batch_size,
+                                shuffle=True, repeat=True)
+    dev_iter = SerialIterator(dev_data, batch_size=batch_size,
+                              shuffle=False, repeat=False)
+
+    optimizer = chainer.optimizers.Adam()
+    optimizer.setup(model)
+    updater = chainer.training.StandardUpdater(train_iter, optimizer)
+    trainer = chainer.training.Trainer(updater, (20, 'epoch'), out='result')
+
+    trainer.extend(extensions.Evaluator(dev_iter, model))
+    trainer.extend(extensions.LogReport())
+    trainer.extend(extensions.PrintReport([
+        'epoch', 'main/accuracy', 'validation/main/accuracy']))
+    trainer.extend(extensions.ProgressBar())
+    
+    trainer.run()
+
+
+def evaluate(model_dir, texts, labels, max_length=100):
+    def create_pipeline(nlp):
+        '''
+        This could be a lambda, but named functions are easier to read in Python.
+        '''
+        return [nlp.tagger, nlp.parser, SentimentAnalyser.load(model_dir, nlp,
+                                                               max_length=max_length)]
+    
+    nlp = spacy.load('en')
+    nlp.pipeline = create_pipeline(nlp)
+
+    correct = 0
+    i = 0 
+    for doc in nlp.pipe(texts, batch_size=1000, n_threads=4):
+        correct += bool(doc.sentiment >= 0.5) == bool(labels[i])
+        i += 1
+    return float(correct) / i
+
+
+@plac.annotations(
+    train_dir=("Location of training file or directory"),
+    dev_dir=("Location of development file or directory"),
+    model_dir=("Location of output model directory",),
+    is_runtime=("Demonstrate run-time usage", "flag", "r", bool),
+    nr_hidden=("Number of hidden units", "option", "H", int),
+    max_length=("Maximum sentence length", "option", "L", int),
+    dropout=("Dropout", "option", "d", float),
+    learn_rate=("Learn rate", "option", "e", float),
+    nb_epoch=("Number of training epochs", "option", "i", int),
+    batch_size=("Size of minibatches for training LSTM", "option", "b", int),
+    nr_examples=("Limit to N examples", "option", "n", int)
+)
+def main(model_dir, train_dir, dev_dir,
+         is_runtime=False,
+         nr_hidden=64, max_length=100, # Shape
+         dropout=0.5, learn_rate=0.001, # General NN config
+         nb_epoch=5, batch_size=32, nr_examples=-1):  # Training params
+    model_dir = pathlib.Path(model_dir)
+    train_dir = pathlib.Path(train_dir)
+    dev_dir = pathlib.Path(dev_dir)
+    if is_runtime:
+        dev_texts, dev_labels = read_data(dev_dir)
+        acc = evaluate(model_dir, dev_texts, dev_labels, max_length=max_length)
+        print(acc)
+    else:
+        print("Read data")
+        train_texts, train_labels = read_data(train_dir, limit=nr_examples)
+        dev_texts, dev_labels = read_data(dev_dir, limit=nr_examples)
+        train_labels = np.asarray(train_labels, dtype='int32')
+        dev_labels = np.asarray(dev_labels, dtype='int32')
+        lstm = train(train_texts, train_labels, dev_texts, dev_labels,
+                     {'nr_hidden': nr_hidden, 'max_length': max_length, 'nr_class': 2,
+                      'nr_vector': 2000, 'nr_dim': 32},
+                     {'dropout': 0.5, 'lr': learn_rate},
+                     {},
+                     nb_epoch=nb_epoch, batch_size=batch_size)
+
+
+if __name__ == '__main__':
+    plac.call(main)