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--- |
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library_name: tf-keras |
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--- |
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x100 smaller with less than 0.5 accuracy drop vs. distilbert-base-uncased-finetuned-sst-2-english |
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## Model description |
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2 Layers Bilstm model finetuned on SST-2 and distlled from RoBERTa teacher |
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distilbert-base-uncased-finetuned-sst-2-english: 92.2 accuracy, 67M parameters |
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moshew/distilbilstm-finetuned-sst-2-english: 91.9 accuracy, 0.66M parameters |
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## How to get started with the model |
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Example on SST-2 test dataset classification: |
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​​ |
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```python |
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!pip install datasets |
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from datasets import load_dataset |
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import numpy as np |
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from sklearn.metrics import accuracy_score |
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from keras.preprocessing.text import Tokenizer |
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from keras.utils import pad_sequences |
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import tensorflow as tf |
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from huggingface_hub import from_pretrained_keras |
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from datasets import load_dataset |
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sst2 = load_dataset("SetFit/sst2") |
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augmented_sst2_dataset = load_dataset("jmamou/augmented-glue-sst2") |
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# Tokenize our training data |
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tokenizer = Tokenizer(num_words=10000) |
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tokenizer.fit_on_texts(augmented_sst2_dataset['train']['sentence']) |
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# Encode test data sentences into sequences |
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test_sequences = tokenizer.texts_to_sequences(sst2['test']['text']) |
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# Pad the test sequences |
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test_padded = pad_sequences(test_sequences, padding = 'post', truncating = 'post', maxlen=64) |
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reloaded_model = from_pretrained_keras('moshew/distilbilstm-finetuned-sst-2-english') |
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#Evaluate model on SST2 test data (GLUE) |
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pred=reloaded_model.predict(test_padded) |
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pred_bin = np.argmax(pred,1) |
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accuracy_score(pred_bin, sst2['test']['label']) |
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0.9187259747391543 |
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reloaded_model.summary() |
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Model: "model" |
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_________________________________________________________________ |
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Layer (type) Output Shape Param # |
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================================================================= |
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input_1 (InputLayer) [(None, 64)] 0 |
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embedding (Embedding) (None, 64, 50) 500000 |
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bidirectional (Bidirectiona (None, 64, 128) 58880 |
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l) |
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bidirectional_1 (Bidirectio (None, 128) 98816 |
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nal) |
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dropout (Dropout) (None, 128) 0 |
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dense (Dense) (None, 2) 258 |
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================================================================= |
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Total params: 657,954 |
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Trainable params: 657,954 |
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Non-trainable params: 0 |
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_________________________________________________________________ |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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| Hyperparameters | Value | |
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| :-- | :-- | |
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| name | Adam | |
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| learning_rate | 0.0010000000474974513 | |
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| decay | 0.0 | |
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| beta_1 | 0.8999999761581421 | |
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| beta_2 | 0.9990000128746033 | |
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| epsilon | 1e-07 | |
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| amsgrad | False | |
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| training_precision | float32 | |
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## Model Plot |
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<details> |
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<summary>View Model Plot</summary> |
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</details> |
