https://github.com/jackyzha0/treehacks2020-backend
[🏆 Azure Prize at TreeHacks] readAR -- 🌲 TreeHacks 2020 Backend
https://github.com/jackyzha0/treehacks2020-backend
azure bert flask treehacks word-sense-disambiguation wsd
Last synced: 3 months ago
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[🏆 Azure Prize at TreeHacks] readAR -- 🌲 TreeHacks 2020 Backend
- Host: GitHub
- URL: https://github.com/jackyzha0/treehacks2020-backend
- Owner: jackyzha0
- Created: 2020-02-15T18:45:32.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-02-17T22:21:01.000Z (over 5 years ago)
- Last Synced: 2025-05-19T08:49:53.791Z (5 months ago)
- Topics: azure, bert, flask, treehacks, word-sense-disambiguation, wsd
- Language: Python
- Homepage: https://devpost.com/software/readar-twh41m?fbclid=IwAR0QCuTZttAxUpiMn0I60vGjkbjpt65ha5JKyDOS-Qz7dcf3cRuCn-zZTVs
- Size: 9.38 MB
- Stars: 5
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# readAR - backend
[DevPost](https://devpost.com/software/readar-twh41m)
This repository contains the implementation of readAR's backend. You can find an implementation of BERT to peform word sense disambiguation served through a Flask API, as well as our image processing pipeline (`/API-azure-pipeline`).
Word Sense Disambiguation is the problem of determining which "sense" (meaning) of a word in the context of a sentence. This model is fine-tuned on the SemEval-2007 dataset and achieves 76.6% F1% score on the test dataset (`semcor.xml`). This is comparable to the current SOTA which achieves 81.2% F1% on the same dataset. The work here builds upon this paper,
> Wiedemann, G., Remus, S., Chawla, A., Biemann, C. (2019): [Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings. Proceedings of KONVENS 2019](https://www.inf.uni-hamburg.de/en/inst/ab/lt/publications/2019-wiedemannetal-bert-sense.pdf), Erlangen, Germany.
### Running the example
The entire working model can be found as a Docker image. You can download it and run it as follows:
1. `docker pull jzhao2k19/bert-wsd:latest`
2. `docker run -p 5000:5000 jzhao2k19/bert-wsd:latest`
## How to train a new model
Run `chmod +x train.sh` to be able to run the script, then do `./train.sh` to begin training. It will take approximately 2 hours to retrain the embeddings. This will generate a new set of weights in a file called `BERT_semcor.pickle`.
#### Serving the model (without Docker)
Run `python server.py` to start a Flask server on `localhost:5000`. Hit it with a POST on `/api/wsd` with a form response containing a `sentence` and target `word`. First spin-up will take ~10s and any subsequent requests will take around 500ms. Keep in mind that running this will have required you to have trained a model before hand. If you would like to run one without training, look at `Running the example` for how to do it through docker.
## Service URLs
(may not work after the hackathon)
* WSD-model: `140.238.147.73:5000/api/wsd`
* img-pipeline: `140.238.147.73:8080/api`
* quiz-generation: `140.238.147.73:8081/api?q=some+query`
## Examples
### 1 -- physics definition of work
```bash
curl --location --request POST '140.238.147.73:5000/api/wsd' \
--form 'sentence=How much work is done to lift a 3kg object 2 meters' \
--form 'word=work'
```
```json
{
"def": "(physics) a manifestation of energy; the transfer of energy from one physical system to another expressed as the product of a force and the distance through which it moves a body in the direction of that force"
}
```
### 1 -- an occupational definition of work
```bash
curl --location --request POST '140.238.147.73:5000/api/wsd' \
--form 'sentence=What do you do for work?' \
--form 'word=work'
```
```json
{
"def": "the occupation for which you are paid"
}
```
### 2 -- a river bank
```bash
curl --location --request POST '140.238.147.73:5000/api/wsd' \
--form 'sentence=I stand on the river bank' \
--form 'word=bank'
```
```json
{
"def": "sloping land (especially the slope beside a body of water)"
}
```
### 2 -- a financial institution
```bash
curl --location --request POST '140.238.147.73:5000/api/wsd' \
--form 'sentence=I need to deposit money at the bank tomorrow' \
--form 'word=bank'
```
```json
{
"def": "a financial institution that accepts deposits and channels the money into lending activities"
}
```