How to train spacy text classification with different labels from a dataframe?

Question

I would like to train the spacy text classifier using labels and words from a dataframe. But I can't get right the training_data and pass it to train.

---

Dataframe example:

category    word    score
0   anger   fasten  0.0
1   anger   morals  1.0
2   anger   tributary   0.0
3   anger   changer 0.0
4   anger   morality    0.0
... ... ... ...
184125  trust   amber   0.0
184126  trust   pulmonary   0.0
184127  trust   ambient 0.0
184128  trust   amaze   0.0
184129  trust   zoom    0.0

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SAMPLE CODE

TRAIN_DATA = [
 # HERE THE TRAIN DATA FROM THE DATAFRAME
 # anger : words related with anger
 # trust : words related with trust
]

nlp = spacy.load("en_core_web_sm")
category = nlp.create_pipe("textcat", config={"exclusive_classes": True})
nlp.add_pipe(category)

# add label to text classifier
category.add_label("Cat")
category.add_label('False')

optimizer = nlp.begin_training()
losses = {}

for i in range(100):
    random.shuffle(TRAIN_DATA)

    for batch in minibatch(TRAIN_DATA, size=8):
        texts = [nlp(text) for text, entities in batch]
        annotations = [{"cats": entities} for text, entities in batch]
        nlp.update(texts, annotations, sgd=optimizer, losses=losses)
    print(i, losses)

EXPECTED OUTPUT:

doc = nlp(u'confidence')  -->  prediction : trust

Answer 1

So I make the training_data format with this code, it seems is taking 4 hours per each training though.

def cat_dict_funct(cat_dict, n):
    for i in range(8):
        if i == n:
            cat_dict[lexicon_labels[i]] = 1
        else:
            cat_dict[lexicon_labels[i]] = 0


train_data = df
train_texts = train_data['word'].tolist()
train_cats = train_data['category'].tolist()

final_train_cats, cat_dict = [], {}

for cat in train_cats: 
    if cat == 'trust':
        cat_dict_funct(cat_dict, 0)
    elif cat == 'fear':
        cat_dict_funct(cat_dict, 1)
    elif cat == 'disgust':
        cat_dict_funct(cat_dict, 2)
    elif cat == 'surprise':
        cat_dict_funct(cat_dict, 3)
    elif cat == 'anticipation':
        cat_dict_funct(cat_dict, 4)
    elif cat == 'anger':
        cat_dict_funct(cat_dict, 5)
    elif cat == 'joy':
        cat_dict_funct(cat_dict, 6)
    else:
        cat_dict_funct(cat_dict, 7)
    final_train_cats.append(cat_dict)

TRAIN_DATA = list(zip(train_texts, [{"cats": cats} for cats in final_train_cats]))