x100 smaller with less than 0.5 accuracy drop vs. distilbert-base-uncased-finetuned-sst-2-english

Model description

2 Layers Bilstm model finetuned on SST-2 and distlled from RoBERTa teacher

distilbert-base-uncased-finetuned-sst-2-english: 92.2 accuracy, 67M parameters
moshew/distilbilstm-finetuned-sst-2-english: 91.9 accuracy, 0.66M parameters

How to get started with the model

Example on SST-2 test dataset classification: ​​

!pip install datasets
from datasets import load_dataset 
import numpy as np 
from sklearn.metrics import accuracy_score 
from keras.preprocessing.text import Tokenizer
from keras.utils import pad_sequences
import tensorflow as tf
from huggingface_hub import from_pretrained_keras

from datasets import load_dataset
sst2 = load_dataset("SetFit/sst2")
augmented_sst2_dataset = load_dataset("jmamou/augmented-glue-sst2")

# Tokenize our training data
tokenizer = Tokenizer(num_words=10000)
tokenizer.fit_on_texts(augmented_sst2_dataset['train']['sentence'])

# Encode test data sentences into sequences
test_sequences = tokenizer.texts_to_sequences(sst2['test']['text'])

# Pad the test sequences
test_padded = pad_sequences(test_sequences, padding = 'post', truncating = 'post', maxlen=64)

reloaded_model = from_pretrained_keras('moshew/distilbilstm-finetuned-sst-2-english')

#Evaluate model on SST2 test data (GLUE)
pred=reloaded_model.predict(test_padded)
pred_bin = np.argmax(pred,1)
accuracy_score(pred_bin, sst2['test']['label'])

0.9187259747391543

reloaded_model.summary()

Model: "model"
_________________________________________________________________
 Layer (type) Output Shape Param # 
=================================================================
 input_1 (InputLayer) [(None, 64)] 0 
 
 embedding (Embedding) (None, 64, 50) 500000 
 
 bidirectional (Bidirectiona (None, 64, 128) 58880 
 l) 
 
 bidirectional_1 (Bidirectio (None, 128) 98816 
 nal) 
 
 dropout (Dropout) (None, 128) 0 
 
 dense (Dense) (None, 2) 258 
 
=================================================================
Total params: 657,954
Trainable params: 657,954
Non-trainable params: 0
_________________________________________________________________

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

Model Plot