How to apply random forests to the output produced by Bert?
I'm trying to get the output embeddings of a RoBERTa model, so I can train a random forests classifier on it for text classification (sentiment analysis). The original dataset this is based on is 500 news articles that each have a left/center/right bias rating. 80% of this dataset is training data, the other 20% is test data.
I run the following code for my training set:
# Tokenize sentences van trainingset 
encoded_input = tokenizer(X_train, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling 
sentence_embeddings = mean_pooling(model_output,encoded_input['attention_mask'])

## Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=3, dim=1)

start = True
numpy_emb = [] 

if not start:
    np_emb = sentence_embeddings.cpu().detach().numpy()
    numpy_emb = np.vstack([numpy_emb, np_emb]) 
else:
    start = False
    numpy_emb = np_emb = sentence_embeddings.cpu().detach().numpy()

Which gives me numpy_emb. Which I think are the embeddings that the RoBERTa model outputs.
When I print it, it gives me:
tensor([[ 0.5329, -0.1224],
        [ 0.5409, -0.0730],
        [ 0.4594, -0.1282],
        [ 0.5116, -0.0769],
        [ 0.4861, -0.0212],
        [ 0.5246, -0.0560],
        [ 0.5555, -0.0962],
        [ 0.4779, -0.0551],
        [ 0.5428, -0.0904],
        [ 0.5939, -0.0504],
        [ 0.5219, -0.1342],
        [ 0.4672, -0.0936],
        [ 0.5051, -0.0518],
        [ 0.5536, -0.1016],
        [ 0.4761, -0.0736],
        [ 0.4754, -0.0991],
        [ 0.5613, -0.0541],
        [ 0.5155,  0.0303],
        [ 0.6053,  0.0214],
        [ 0.4766, -0.1019],
        [ 0.4262, -0.0869],
        [ 0.3871, -0.0756],
        [ 0.5048, -0.0067],
        [ 0.5425, -0.1303],
        [ 0.5020, -0.0715],
    ...
        [ 0.5462, -0.0686],
        [ 0.5476, -0.1465],
        [ 0.4968, -0.0354],
        [ 0.5586, -0.1234],
        [ 0.5725, -0.0685]])

I then repeat this process for my test set as well, giving me another set of embeddings.
Then I try to train a random forests classifier using the embeddings given by the training set. But when I try to predict using the embeddings from my test set, I get very random results. Accuracy goes as low as 24% and as high as 58%. Is this because of the small amount of data that I have? Or is there something else I'm doing wrong?
I also have the suspicion that I can't properly link the output embedding to their respective label. Which would also explain the random results I get.
Code for random forests that I used:
from sklearn.ensemble import RandomForestClassifier
text_classifier = RandomForestClassifier(n_estimators=100, random_state=0)
text_classifier.fit(numpy_emb, y_train)

predictions = text_classifier.predict(numpy_emb_test)

#confusion matrix
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
print(confusion_matrix(y_test,predictions))
print(classification_report(y_test,predictions))


This shape doesn't look like proper embedding. For classification purpose, a usual approach to encoder-only models is just supplying the last hidden state as embeddings for the classifier, for example:
features = model_output[0][:,0,:].numpy()
text_classifier.fit(features, y_train)

