
# BERT multilingual base model (cased)

Pretrained model on the English dataset using a masked language modeling (MLM) objective.
It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is case sensitive: it makes a difference
between english and English.

## Model description

BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. 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. More precisely, it
was pretrained with two objectives:

- Masked language modeling (MLM): 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.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
  predict if the two sentences were following each other or not.

The pretrained model has been finetuned for one specific language for one specific task. 

### How to use

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

```python
from transformers import BertTokenizer, BertModel
model = BertModel.from_pretrained("mushfiqur11/<repo_name>")
```

{"language": ["en"], "license": "apache-2.0"}
transformers pytorch bert token-classification en arxiv:1810.04805 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #en #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# BERT multilingual base model (cased)

Pretrained model on the English dataset using a masked language modeling (MLM) objective.
It was introduced in this paper and first released in
this repository. This model is case sensitive: it makes a difference
between english and English.

## Model description

BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. 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. More precisely, it
was pretrained with two objectives:

- Masked language modeling (MLM): 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.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
  predict if the two sentences were following each other or not.

The pretrained model has been finetuned for one specific language for one specific task. 

### How to use

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



# BERT multilingual base model (cased)

Pretrained model on the English dataset using a masked language modeling (MLM) objective.
It was introduced in this paper and first released in
this repository. This model is case sensitive: it makes a difference
between english and English. ## Model description

BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. 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. More precisely, it
was pretrained with two objectives:

- Masked language modeling (MLM): 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.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
  predict if the two sentences were following each other or not.

The pretrained model has been finetuned for one specific language for one specific task. ### How to use

Here is how to use this model to get the features of a given text in PyTorch:
TAGS
#transformers #pytorch #bert #token-classification #en #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # BERT multilingual base model (cased)

Pretrained model on the English dataset using a masked language modeling (MLM) objective.
It was introduced in this paper and first released in
this repository. This model is case sensitive: it makes a difference
between english and English. ## Model description

BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. 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. More precisely, it
was pretrained with two objectives:

- Masked language modeling (MLM): 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.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
  predict if the two sentences were following each other or not.

The pretrained model has been finetuned for one specific language for one specific task. ### How to use

Here is how to use this model to get the features of a given text in PyTorch:
TAGS
#transformers #pytorch #bert #token-classification #en #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# BERT multilingual base model (cased)

Pretrained model on the English dataset using a masked language modeling (MLM) objective.
It was introduced in this paper and first released in
this repository. This model is case sensitive: it makes a difference
between english and English.## Model description

BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. 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. More precisely, it
was pretrained with two objectives:

- Masked language modeling (MLM): 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.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
  predict if the two sentences were following each other or not.

The pretrained model has been finetuned for one specific language for one specific task.### How to use

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