language: en
license: apache-2.0
tags:
- text-classification
datasets:
- sst2
metrics:
- accuracy
bert-base-uncased model fine-tuned on SST-2
This model was created using the nn_pruning python library: the linear layers contains 37% of the original weights.
The model contains 51% of the original weights overall (the embeddings account for a significant part of the model, and they are not pruned by this method).
In terms of perfomance, its accuracy is 91.17.
Fine-Pruning details
This model was fine-tuned from the HuggingFace model checkpoint on task, and distilled from the model textattack/bert-base-uncased-SST-2. This model is case-insensitive: it does not make a difference between english and English.
A side-effect of the block pruning method is that some of the attention heads are completely removed: 88 heads were removed on a total of 144 (61.1%). Here is a detailed view on how the remaining heads are distributed in the network after pruning.
Details of the SST-2 dataset
| Dataset | Split | # samples |
|---|---|---|
| SST-2 | train | 67K |
| SST-2 | eval | 872 |
Results
Pytorch model file size: 351MB (original BERT: 420MB)
| Metric | # Value | # Original (Table 2) | Variation |
|---|---|---|---|
| accuracy | 91.17 | 92.7 | -1.53 |
Example Usage
Install nn_pruning: it contains the optimization script, which just pack the linear layers into smaller ones by removing empty rows/columns.
pip install nn_pruning
Then you can use the transformers library almost as usual: you just have to call optimize_model when the pipeline has loaded.
from transformers import pipeline
from nn_pruning.inference_model_patcher import optimize_model
cls_pipeline = pipeline(
"text-classification",
model="echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid",
tokenizer="echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid",
)
print(f"Parameters count (includes only head pruning, no feed forward pruning)={int(cls_pipeline.model.num_parameters() / 1E6)}M")
cls_pipeline.model = optimize_model(cls_pipeline.model, "dense")
print(f"Parameters count after optimization={int(cls_pipeline.model.num_parameters() / 1E6)}M")
predictions = cls_pipeline("This restaurant is awesome")
print(predictions)