
## bert-base-uncased model fine-tuned on SST-2

This model was created using the [nn_pruning](https://github.com/huggingface/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).

<div class="graph"><script src="/echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid/raw/main/model_card/density_info.js" id="2d0fc334-fe98-4315-8890-d6eaca1fa9be"></script></div>

In terms of perfomance, its **accuracy** is **91.17**.

## Fine-Pruning details
This model was fine-tuned from the HuggingFace [model](https://huggingface.co/bert-base-uncased) checkpoint on task, and distilled from the model [textattack/bert-base-uncased-SST-2](https://huggingface.co/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.
<div class="graph"><script src="/echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid/raw/main/model_card/pruning_info.js" id="93b19d7f-c11b-4edf-9670-091e40d9be25"></script></div>

## 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](https://www.aclweb.org/anthology/N19-1423.pdf))| 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.

```python
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)
```

{"language": "en", "license": "apache-2.0", "tags": ["text-classification"], "datasets": ["sst2"], "metrics": ["accuracy"]}
transformers pytorch bert text-classification en dataset:sst2 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

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: SST-2, Split: train, # samples: 67K
Dataset: SST-2, Split: eval, # samples: 872


### Results


Pytorch model file size: '351MB' (original BERT: '420MB')



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.



# samples: 67K
Dataset: SST-2, Split: eval, # samples: 872 ### Results


Pytorch model file size: '351MB' (original BERT: '420MB')



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.
TAGS
#transformers #pytorch #bert #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # samples: 67K
Dataset: SST-2, Split: eval, # samples: 872 ### Results


Pytorch model file size: '351MB' (original BERT: '420MB')



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.
passage: TAGS
#transformers #pytorch #bert #text-classification #en #dataset-sst2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# samples: 67K
Dataset: SST-2, Split: eval, # samples: 872### Results


Pytorch model file size: '351MB' (original BERT: '420MB')



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.