Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs
| Date | Stars |
|---|---|
| 2026-07-24 | 367 |
| 2026-07-25 | 367 |
| 2026-07-28 | 367 |
| 2026-07-30 | 367 |
| 2026-08-06 | 367 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
#### Better NER [BERT Named-Entity-Recognition](https://github.com/kamalkraj/BERT-NER)
# Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs
A keras implementation of Bidirectional-LSTM_CNNs for Named-Entity-Recoganition. The original paper can be found at https://arxiv.org/abs/1511.08308
The implementation differs from the original paper in the following ways :
1) lexicons are not considered
2) Bucketing is used to speed up the training
3) nadam optimizer used instead of SGD
# Result
The model produces a test F1_score of 90.9 % with ~70 epochs. The results produced in the paper for the given architecture is 91.14
Architecture(BILSTM-CNN with emb + caps)
# Dataset
### conll-2003
# Network Model in paper
<img src="https://raw.githubusercontent.com/kamalkraj/Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs/master/model_on_paper.png"/> <img src="https://raw.githubusercontent.com/kamalkraj/Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs/master/char_embeddings.png"/>
# Network Model Constructed Using Keras
## To run the script
```bash
python3 nn.py
```
## Requirements
0) nltk
1) numpy
2) Keras==2.1.2
3) Tensorflow==1.4.1
## Inference on trained model
```python
from ner import Parser
p = Parser()
p.load_models("models/")
p.predict("Steve Went to Paris")
##Output [('Steve', 'B-PER'), ('went', 'O'), ('to', 'O'), ('Paris', 'B-LOC')]
```
Excerpt of 1,560 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:60081fd860b95fe6, topic:named-entity-recognition, name:named entity recognition, desc:named entity recognition
matched fp:60081fd860b95fe6, topic:tensorflow