
# Cloud4bert

This model is a specialised version of the [BERT base model](https://huggingface.co/ultraleow/cloud4bert). The code for the training process will be uploaded
[here](https://huggingface.co/ultraleow/cloud4bert/). This model is uncased: it does not make a difference between english and English.

## Model description

Cloud4bert is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic
process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained
with three objectives:

- Distillation loss: the model was trained to return the same probabilities as the BERT base model.
- Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a
  sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the
  model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that
  usually see the words one after the other, or from autoregressive models like GPT which internally mask the future
  tokens. It allows the model to learn a bidirectional representation of the sentence.
- Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base
  model.

This way, the model learns the same inner representation of the English language than its teacher model, while being
faster for inference or downstream tasks.

## Intended uses & limitations

will be added soon

### How to use

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

```python
>>> from transformers import pipeline
>>> sentiment_analzyor = pipeline('text-classification', model='ultraleow/cloud4bert')
>>> sentiment_analzyor("Sorry, I don't understand - are you saying you don't have the `paypal` section defined? You need to, otherwise, it's an 'unknown element' in the web.config.")
[{'label': 'LABEL_0', 'score': 0.6916515231132507}]
#LABEL_0 = negative
#LABEL_1 = neutral
#LABEL_2 = positive
```

Here is how to use this model to get the features of a given text in PyTorch:

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ultraleow/cloud4bert")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

and in TensorFlow:

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ultraleow/cloud4bert")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```



## Training data

will be added soon

## Training procedure

will be added soon

### Preprocessing

will be added soon


### Pretraining

will be added soon


## Evaluation results

When fine-tuned on downstream tasks, this model achieves the following results:

Glue test results:

| Task | Recall(Weighted) | Precision(Weighted)  | f1(Weighted) | ACC |
|:----:|:----:|:----:|:----:|:-----:|
|      | 94.03% | 94.06% | 94.02% | 94.03%  | 

{"language": "en", "license": "openrail", "metrics": ["accuracy", "precision", "recall", "f1"], "pipeline_tag": "text-classification"}
transformers pytorch bert text-classification en license:openrail autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #en #license-openrail #autotrain_compatible #endpoints_compatible #region-us 

Cloud4bert
==========


This model is a specialised version of the BERT base model. The code for the training process will be uploaded
here. This model is uncased: it does not make a difference between english and English.


Model description
-----------------


Cloud4bert is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic
process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained
with three objectives:


* Distillation loss: the model was trained to return the same probabilities as the BERT base model.
* Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a
sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the
model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that
usually see the words one after the other, or from autoregressive models like GPT which internally mask the future
tokens. It allows the model to learn a bidirectional representation of the sentence.
* Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base
model.


This way, the model learns the same inner representation of the English language than its teacher model, while being
faster for inference or downstream tasks.


Intended uses & limitations
---------------------------


will be added soon


### How to use


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


Here is how to use this model to get the features of a given text in PyTorch:


and in TensorFlow:


Training data
-------------


will be added soon


Training procedure
------------------


will be added soon


### Preprocessing


will be added soon


### Pretraining


will be added soon


Evaluation results
------------------


When fine-tuned on downstream tasks, this model achieves the following results:


Glue test results:




### How to use


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


Here is how to use this model to get the features of a given text in PyTorch:


and in TensorFlow:


Training data
-------------


will be added soon


Training procedure
------------------


will be added soon ### Preprocessing


will be added soon ### Pretraining


will be added soon


Evaluation results
------------------


When fine-tuned on downstream tasks, this model achieves the following results:


Glue test results:
TAGS
#transformers #pytorch #bert #text-classification #en #license-openrail #autotrain_compatible #endpoints_compatible #region-us 
 ### How to use


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


Here is how to use this model to get the features of a given text in PyTorch:


and in TensorFlow:


Training data
-------------


will be added soon


Training procedure
------------------


will be added soon ### Preprocessing


will be added soon ### Pretraining


will be added soon


Evaluation results
------------------


When fine-tuned on downstream tasks, this model achieves the following results:


Glue test results:
TAGS
#transformers #pytorch #bert #text-classification #en #license-openrail #autotrain_compatible #endpoints_compatible #region-us 
### How to use


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


Here is how to use this model to get the features of a given text in PyTorch:


and in TensorFlow:


Training data
-------------


will be added soon


Training procedure
------------------


will be added soon### Preprocessing


will be added soon### Pretraining


will be added soon


Evaluation results
------------------


When fine-tuned on downstream tasks, this model achieves the following results:


Glue test results: