Flair_NER/README.md

# NER (Named Entity Recognition)

## Requirements
````shell
pip install flair
````
## Download Models
download models and place in data folder
https://drive.google.com/file/d/1mBW3zA8sd1zDo7KOiUCXmG64h8eJc_ip/view

## Getting started

````shell
python flair_ner_inference_.py
````

for train:

````shell
python flair_ner_train.py
````

## Documentation

Flair is:

* **A powerful NLP library.** Flair allows you to apply our state-of-the-art natural language processing (NLP)
models to your text, such as named entity recognition (NER), sentiment analysis, part-of-speech tagging (PoS),
  special support for [biomedical data](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md),
 sense disambiguation and classification, with support for a rapidly growing number of languages.

* **A text embedding library.** Flair has simple interfaces that allow you to use and combine different word and
document embeddings, including our proposed [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and various transformers.

* **A PyTorch NLP framework.** Our framework builds directly on [PyTorch](https://pytorch.org/), making it easy to
train your own models and experiment with new approaches using Flair embeddings and classes.


## Quick Start Flair

### Requirements and Installation

In your favorite virtual environment, simply do:

```
pip install flair
```

Flair requires Python 3.7+. 

### Example 1: Tag Entities in Text

Let's run **named entity recognition** (NER) over an example sentence. All you need to do is make a `Sentence`, load
a pre-trained model and use it to predict tags for the sentence:

```python
from flair.data import Sentence
from flair.nn import Classifier

# make a sentence
sentence = Sentence('I love Berlin .')

# load the NER tagger
tagger = Classifier.load('ner')

# run NER over sentence
tagger.predict(sentence)

# print the sentence with all annotations
print(sentence)
```

This should print:

```console
Sentence: "I love Berlin ." → ["Berlin"/LOC]
```

This means that "Berlin" was tagged as a **location entity** in this sentence. 

   * *to learn more about NER tagging in Flair, check out our [NER tutorial](https://flairnlp.github.io/docs/tutorial-basics/tagging-entities)!*


### Example 2: Detect Sentiment 

Let's run **sentiment analysis** over an example sentence to determine whether it is POSITIVE or NEGATIVE.
Same code as above, just a different model: 

```python
from flair.data import Sentence
from flair.nn import Classifier

# make a sentence
sentence = Sentence('I love Berlin .')

# load the NER tagger
tagger = Classifier.load('sentiment')

# run NER over sentence
tagger.predict(sentence)

# print the sentence with all annotations
print(sentence)
```

This should print:

```console
Sentence[4]: "I love Berlin ." → POSITIVE (0.9983)
```

This means that the sentence "I love Berlin" was tagged as having **POSITIVE** sentiment. 

   * *to learn more about sentiment analysis in Flair, check out our [sentiment analysis tutorial](https://flairnlp.github.io/docs/tutorial-basics/tagging-sentiment)!*

## Tutorials

On our new :fire: [**Flair documentation page**](https://flairnlp.github.io/docs/intro) you will find many tutorials to get you started!

In particular: 
- [Tutorial 1: Basic tagging](https://flairnlp.github.io/docs/category/tutorial-1-basic-tagging) → how to tag your text 
- [Tutorial 2: Training models](https://flairnlp.github.io/docs/category/tutorial-2-training-models) → how to train your own state-of-the-art NLP models 
- [Tutorial 3: Embeddings](https://flairnlp.github.io/docs/category/tutorial-3-embeddings) → how to produce embeddings for words and documents

There is also a dedicated landing page for our [biomedical NER and datasets](/resources/docs/HUNFLAIR.md) with
installation instructions and tutorials.