
# NetBERT 📶

<img align="left" src="illustration.jpg" width="150"/>
<br><br><br>

&nbsp;&nbsp;&nbsp;NetBERT is a [BERT-base](https://huggingface.co/bert-base-cased) model further pre-trained on a huge corpus of computer networking text (~23Gb).

<br><br>

## Usage

You can use the raw model for masked language modeling (MLM), but it's mostly intended to be fine-tuned on a downstream task, especially one that uses the whole sentence to make decisions such as text classification, extractive question answering, or semantic search.

You can use this model directly with a pipeline for [masked language modeling](https://huggingface.co/tasks/fill-mask):

```python
from transformers import pipeline

unmasker = pipeline('fill-mask', model='antoinelouis/netbert')
unmasker("The nodes of a computer network may include [MASK].")
```

You can also use this model to [extract the features](https://huggingface.co/tasks/feature-extraction) of a given text:

```python
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained('antoinelouis/netbert')
model = AutoModel.from_pretrained('antoinelouis/netbert')

text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

## Documentation

Detailed documentation on the pre-trained model, its implementation, and the data can be found on [Github](https://github.com/antoiloui/netbert/blob/master/docs/index.md).

## Citation

For attribution in academic contexts, please cite this work as:

```
@mastersthesis{louis2020netbert,
    title={NetBERT: A Pre-trained Language Representation Model for Computer Networking},
    author={Louis, Antoine},
    year={2020},
    school={University of Liege}
}
```
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "widget": [{"text": "The nodes of a computer network may include [MASK]."}]}
transformers pytorch tf jax bert fill-mask en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# NetBERT 

<img align="left" src="URL" width="150"/>
<br><br><br>

&nbsp;&nbsp;&nbsp;NetBERT is a BERT-base model further pre-trained on a huge corpus of computer networking text (~23Gb).

<br><br>

## Usage

You can use the raw model for masked language modeling (MLM), but it's mostly intended to be fine-tuned on a downstream task, especially one that uses the whole sentence to make decisions such as text classification, extractive question answering, or semantic search.

You can use this model directly with a pipeline for masked language modeling:



You can also use this model to extract the features of a given text:



## Documentation

Detailed documentation on the pre-trained model, its implementation, and the data can be found on Github.

For attribution in academic contexts, please cite this work as:


# NetBERT 

<img align="left" src="URL" width="150"/>
<br><br><br>

&nbsp;&nbsp;&nbsp;NetBERT is a BERT-base model further pre-trained on a huge corpus of computer networking text (~23Gb).

<br><br> ## Usage

You can use the raw model for masked language modeling (MLM), but it's mostly intended to be fine-tuned on a downstream task, especially one that uses the whole sentence to make decisions such as text classification, extractive question answering, or semantic search.

You can use this model directly with a pipeline for masked language modeling:



You can also use this model to extract the features of a given text: ## Documentation

Detailed documentation on the pre-trained model, its implementation, and the data can be found on Github.

For attribution in academic contexts, please cite this work as:
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # NetBERT 

<img align="left" src="URL" width="150"/>
<br><br><br>

&nbsp;&nbsp;&nbsp;NetBERT is a BERT-base model further pre-trained on a huge corpus of computer networking text (~23Gb).

<br><br> ## Usage

You can use the raw model for masked language modeling (MLM), but it's mostly intended to be fine-tuned on a downstream task, especially one that uses the whole sentence to make decisions such as text classification, extractive question answering, or semantic search.

You can use this model directly with a pipeline for masked language modeling:



You can also use this model to extract the features of a given text: ## Documentation

Detailed documentation on the pre-trained model, its implementation, and the data can be found on Github.

For attribution in academic contexts, please cite this work as:
passage: TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# NetBERT 

<img align="left" src="URL" width="150"/>
<br><br><br>

&nbsp;&nbsp;&nbsp;NetBERT is a BERT-base model further pre-trained on a huge corpus of computer networking text (~23Gb).

<br><br>## Usage

You can use the raw model for masked language modeling (MLM), but it's mostly intended to be fine-tuned on a downstream task, especially one that uses the whole sentence to make decisions such as text classification, extractive question answering, or semantic search.

You can use this model directly with a pipeline for masked language modeling:



You can also use this model to extract the features of a given text:## Documentation

Detailed documentation on the pre-trained model, its implementation, and the data can be found on Github.

For attribution in academic contexts, please cite this work as: