## Tensorflow Keras Implementation of Named Entity Recognition using Transformers.

This repo contains code using the model. [Named Entity Recognition using Transformers](https://keras.io/examples/nlp/ner_transformers/).

Credits: [Varun Singh](https://www.linkedin.com/in/varunsingh2/) - Original Author

HF Contribution: [Rishav Chandra Varma](https://huggingface.co/reichenbach)  

## Background Information

### Introduction

Named Entity Recognition (NER) is the process of identifying named entities in text. Example of named entities are: "Person", "Location", "Organization", "Dates" etc. NER is essentially a token classification task where every token is classified into one or more predetermined categories.

We will train a simple Transformer based model to perform NER. We will be using the data from CoNLL 2003 shared task. For more information about the dataset, please visit the [dataset website](https://www.clips.uantwerpen.be/conll2003/ner/). However, since obtaining this data requires an additional step of getting a free license, we will be using HuggingFace's datasets library which contains a processed version of this [dataset](https://huggingface.co/datasets/conll2003).

{"tags": ["multimodal-entailment", "generic"]}
keras multimodal-entailment generic has_space region:us
TAGS
#keras #multimodal-entailment #generic #has_space #region-us 

## Tensorflow Keras Implementation of Named Entity Recognition using Transformers.

This repo contains code using the model. Named Entity Recognition using Transformers.

Credits: Varun Singh - Original Author

HF Contribution: Rishav Chandra Varma  

## Background Information

### Introduction

Named Entity Recognition (NER) is the process of identifying named entities in text. Example of named entities are: "Person", "Location", "Organization", "Dates" etc. NER is essentially a token classification task where every token is classified into one or more predetermined categories.

We will train a simple Transformer based model to perform NER. We will be using the data from CoNLL 2003 shared task. For more information about the dataset, please visit the dataset website. However, since obtaining this data requires an additional step of getting a free license, we will be using HuggingFace's datasets library which contains a processed version of this dataset.

## Tensorflow Keras Implementation of Named Entity Recognition using Transformers.

This repo contains code using the model. Named Entity Recognition using Transformers.

Credits: Varun Singh - Original Author

HF Contribution: Rishav Chandra Varma ## Background Information ### Introduction

Named Entity Recognition (NER) is the process of identifying named entities in text. Example of named entities are: "Person", "Location", "Organization", "Dates" etc. NER is essentially a token classification task where every token is classified into one or more predetermined categories.

We will train a simple Transformer based model to perform NER. We will be using the data from CoNLL 2003 shared task. For more information about the dataset, please visit the dataset website. However, since obtaining this data requires an additional step of getting a free license, we will be using HuggingFace's datasets library which contains a processed version of this dataset.
TAGS
#keras #multimodal-entailment #generic #has_space #region-us 
 ## Tensorflow Keras Implementation of Named Entity Recognition using Transformers.

This repo contains code using the model. Named Entity Recognition using Transformers.

Credits: Varun Singh - Original Author

HF Contribution: Rishav Chandra Varma ## Background Information ### Introduction

Named Entity Recognition (NER) is the process of identifying named entities in text. Example of named entities are: "Person", "Location", "Organization", "Dates" etc. NER is essentially a token classification task where every token is classified into one or more predetermined categories.

We will train a simple Transformer based model to perform NER. We will be using the data from CoNLL 2003 shared task. For more information about the dataset, please visit the dataset website. However, since obtaining this data requires an additional step of getting a free license, we will be using HuggingFace's datasets library which contains a processed version of this dataset.
passage: TAGS
#keras #multimodal-entailment #generic #has_space #region-us 
## Tensorflow Keras Implementation of Named Entity Recognition using Transformers.

This repo contains code using the model. Named Entity Recognition using Transformers.

Credits: Varun Singh - Original Author

HF Contribution: Rishav Chandra Varma## Background Information### Introduction

Named Entity Recognition (NER) is the process of identifying named entities in text. Example of named entities are: "Person", "Location", "Organization", "Dates" etc. NER is essentially a token classification task where every token is classified into one or more predetermined categories.

We will train a simple Transformer based model to perform NER. We will be using the data from CoNLL 2003 shared task. For more information about the dataset, please visit the dataset website. However, since obtaining this data requires an additional step of getting a free license, we will be using HuggingFace's datasets library which contains a processed version of this dataset.