Text Classification Using Embeddings ↗

This notebook shows how to build a classifier using Cohere’s embeddings.

The example classification task here will be sentiment analysis of film reviews. We’ll train a simple classifier to detect whether a film review is negative (class 0) or positive (class 1).

We’ll go through the following steps:

1. Get the dataset
2. Get the embeddings of the reviews (for both the training set and the test set).
3. Train a classifier using the training set
4. Evaluate the performance of the classifier on the testing set

If you’re running an older version of the SDK you’ll want to upgrade it, like this:

#!pip install --upgrade cohere

1. Get the dataset#

import cohere
from sklearn.model_selection import train_test_split

import pandas as pd
pd.set_option('display.max_colwidth', None)

df = pd.read_csv('https://github.com/clairett/pytorch-sentiment-classification/raw/master/data/SST2/train.tsv', delimiter='\t', header=None)

df.head()

<td>
  a stirring , funny and finally transporting re imagining of beauty and
  the beast and 1930s horror films
</td>

<td>
  1
</td>

<td>
  apparently reassembled from the cutting room floor of any given
  daytime soap
</td>

<td>
  0
</td>

<td>
  they presume their audience wo n't sit still for a sociology lesson ,
  however entertainingly presented , so they trot out the conventional
  science fiction elements of bug eyed monsters and futuristic women in
  skimpy clothes
</td>

<td>
  0
</td>

<td>
  this is a visually stunning rumination on love , memory , history and
  the war between art and commerce
</td>

<td>
  1
</td>

<td>
  jonathan parker 's bartleby should have been the be all end all of the
  modern office anomie films
</td>

<td>
  1
</td>

<th>
  0
</th>

<th>
  1
</th>

We’ll only use a subset of the training and testing datasets in this example. We’ll only use 500 examples since this is a toy example. You’ll want to increase the number to get better performance and evaluation.

The train_test_split method splits arrays or matrices into random train and test subsets.

num_examples = 500
df_sample = df.sample(num_examples)

sentences_train, sentences_test, labels_train, labels_test = train_test_split(
            list(df_sample[0]), list(df_sample[1]), test_size=0.25, random_state=0)


sentences_train = sentences_train[:95]
sentences_test = sentences_test[:95]

labels_train = labels_train[:95]
labels_test = labels_test[:95]

2. Set up the Cohere client and get the embeddings of the reviews#

We’re now ready to retrieve the embeddings from the API. You’ll need your API key for this next cell. Sign up to Cohere and get one if you haven’t yet.

model_name = "embed-v4.0"
api_key = ""

input_type = "classification"

co = cohere.Client(api_key)

embeddings_train = co.embed(texts=sentences_train,
                            model=model_name,
                            input_type=input_type
                            ).embeddings

embeddings_test = co.embed(texts=sentences_test,
                           model=model_name,
                           input_type=input_type
                            ).embeddings

Note that the ordering of the arguments is important. If you put input_type in before model_name, you’ll get an error.

We now have two sets of embeddings, embeddings_train contains the embeddings of the training sentences while embeddings_test contains the embeddings of the testing sentences.

Curious what an embedding looks like? We can print it:

print(f"Review text: {sentences_train[0]}")
print(f"Embedding vector: {embeddings_train[0][:10]}")

Review text: the script was reportedly rewritten a dozen times either 11 times too many or else too few
Embedding vector: [1.1531117, -0.8543223, -1.2496399, -0.28317127, -0.75870246, 0.5373464, 0.63233083, 0.5766576, 1.8336298, 0.44203663]

3. Train a classifier using the training set#

Now that we have the embedding, we can train our classifier. We’ll use an SVM from sklearn.

from sklearn.svm import SVC
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler


svm_classifier = make_pipeline(StandardScaler(), SVC(class_weight='balanced'))

svm_classifier.fit(embeddings_train, labels_train)

Pipeline(steps=[('standardscaler', StandardScaler()),
                ('svc', SVC(class_weight='balanced'))])

4. Evaluate the performance of the classifier on the testing set#

score = svm_classifier.score(embeddings_test, labels_test)
print(f"Validation accuracy on is {100*score}%!")

Validation accuracy on Large is 91.2%!

You may get a slightly different number when you run this code.

This was a small scale example, meant as a proof of concept and designed to illustrate how you can build a custom classifier quickly using a small amount of labelled data and Cohere’s embeddings. Increase the number of training examples to achieve better performance on this task.