Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

GH-1770: Added a link to a repo with examples of Flair on GCP #1825

Merged
merged 1 commit into from
Aug 24, 2020
Merged

GH-1770: Added a link to a repo with examples of Flair on GCP #1825

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
40 changes: 20 additions & 20 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,15 +14,15 @@ 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), part-of-speech tagging (PoS),
sense disambiguation and classification, with support for a rapidly growing number of languages.
* **A biomedical NER library.** Flair has special support for [biomedical data](/resources/docs/HUNFLAIR.md) with
sense disambiguation and classification, with support for a rapidly growing number of languages.

* **A biomedical NER library.** Flair has special support for [biomedical data](/resources/docs/HUNFLAIR.md) with
state-of-the-art models for biomedical NER and support for over 32 biomedical datasets.

* **A text embedding library.** Flair has simple interfaces that allow you to use and combine different word and
* **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/)**, BERT embeddings and ELMo embeddings.

* **A PyTorch NLP framework.** Our framework builds directly on [PyTorch](https://pytorch.org/), making it easy to
* **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.

Now at [version 0.6](https://github.com/flairNLP/flair/releases)!
Expand All @@ -46,7 +46,7 @@ Flair outperforms the previous best methods on a range of NLP tasks:
Here's how to [reproduce these numbers](/resources/docs/EXPERIMENTS.md) using Flair. You can also find detailed evaluations and discussions in our papers:

* *[Contextual String Embeddings for Sequence Labeling](https://www.aclweb.org/anthology/C18-1139/).
Alan Akbik, Duncan Blythe and Roland Vollgraf.
Alan Akbik, Duncan Blythe and Roland Vollgraf.
27th International Conference on Computational Linguistics, **COLING 2018**.*

* *[Pooled Contextualized Embeddings for Named Entity Recognition](https://www.aclweb.org/anthology/papers/N/N19/N19-1078/).
Expand All @@ -71,7 +71,7 @@ pip install flair

### Example Usage

Let's run named entity recognition (NER) over an example sentence. All you need to do is make a `Sentence`, load
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
Expand Down Expand Up @@ -99,12 +99,12 @@ for entity in sentence.get_spans('ner'):
print(entity)
```

This should print:
This should print:

```console
Sentence: "I love Berlin ." - 4 Tokens

The following NER tags are found:
The following NER tags are found:

Span [3]: "Berlin" [− Labels: LOC (0.9992)]
```
Expand All @@ -121,26 +121,27 @@ We provide a set of quick tutorials to get you started with the library:
* [Tutorial 6: Loading a Dataset](/resources/docs/TUTORIAL_6_CORPUS.md)
* [Tutorial 7: Training a Model](/resources/docs/TUTORIAL_7_TRAINING_A_MODEL.md)
* [Tutorial 8: Training your own Flair Embeddings](/resources/docs/TUTORIAL_9_TRAINING_LM_EMBEDDINGS.md)

The tutorials explain how the base NLP classes work, how you can load pre-trained models to tag your
text, how you can embed your text with different word or document embeddings, and how you can train your own
text, how you can embed your text with different word or document embeddings, and how you can train your own
language models, sequence labeling models, and text classification models. Let us know if anything is unclear.

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

There are also good third-party articles and posts that illustrate how to use Flair:
There are also good third-party articles and posts that illustrate how to use Flair:
* [How to build a text classifier with Flair](https://towardsdatascience.com/text-classification-with-state-of-the-art-nlp-library-flair-b541d7add21f)
* [How to build a microservice with Flair and Flask](https://shekhargulati.com/2019/01/04/building-a-sentiment-analysis-python-microservice-with-flair-and-flask/)
* [A docker image for Flair](https://towardsdatascience.com/docker-image-for-nlp-5402c9a9069e)
* [Great overview of Flair functionality and how to use in Colab](https://www.analyticsvidhya.com/blog/2019/02/flair-nlp-library-python/)
* [Visualisation tool for highlighting the extracted entities](https://github.com/lunayach/visNER)
* [Practical approach of State-of-the-Art Flair in Named Entity Recognition](https://medium.com/analytics-vidhya/practical-approach-of-state-of-the-art-flair-in-named-entity-recognition-46a837e25e6b)
* [Benchmarking NER algorithms](https://towardsdatascience.com/benchmark-ner-algorithm-d4ab01b2d4c3)
* [Training a Flair text classifier on Google Cloud Platform (GCP) and serving predictions on GCP](https://github.com/robinvanschaik/flair-on-gcp)

## Citing Flair

Please cite the following paper when using Flair:
Please cite the following paper when using Flair:

```
@inproceedings{akbik2018coling,
Expand All @@ -164,18 +165,18 @@ If you use the pooled version of the Flair embeddings (PooledFlairEmbeddings), p
}
```

## Contact
## Contact

Please email your questions or comments to [Alan Akbik](http://alanakbik.github.io/).

## Contributing

Thanks for your interest in contributing! There are many ways to get involved;
start with our [contributor guidelines](CONTRIBUTING.md) and then
Thanks for your interest in contributing! There are many ways to get involved;
start with our [contributor guidelines](CONTRIBUTING.md) and then
check these [open issues](https://github.com/flairNLP/flair/issues) for specific tasks.

For contributors looking to get deeper into the API we suggest cloning the repository and checking out the unit
tests for examples of how to call methods. Nearly all classes and methods are documented, so finding your way around
For contributors looking to get deeper into the API we suggest cloning the repository and checking out the unit
tests for examples of how to call methods. Nearly all classes and methods are documented, so finding your way around
the code should hopefully be easy.

### Running unit tests locally
Expand Down Expand Up @@ -210,4 +211,3 @@ Permission is hereby granted, free of charge, to any person obtaining a copy of
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.