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https://github.com/monologg/JointBERT
Pytorch implementation of JointBERT: "BERT for Joint Intent Classification and Slot Filling"
https://github.com/monologg/JointBERT
bert intent-classification joint-bert pytorch slot-filling slu transformers
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Pytorch implementation of JointBERT: "BERT for Joint Intent Classification and Slot Filling"
- Host: GitHub
- URL: https://github.com/monologg/JointBERT
- Owner: monologg
- License: apache-2.0
- Created: 2019-11-14T15:48:12.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-01-11T07:51:17.000Z (12 months ago)
- Last Synced: 2024-08-02T16:55:43.225Z (5 months ago)
- Topics: bert, intent-classification, joint-bert, pytorch, slot-filling, slu, transformers
- Language: Python
- Homepage:
- Size: 464 KB
- Stars: 636
- Watchers: 13
- Forks: 185
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - monologg/JointBERT
README
# JointBERT
(Unofficial) Pytorch implementation of `JointBERT`: [BERT for Joint Intent Classification and Slot Filling](https://arxiv.org/abs/1902.10909)
## Model Architecture
- Predict `intent` and `slot` at the same time from **one BERT model** (=Joint model)
- total_loss = intent_loss + coef \* slot_loss (Change coef with `--slot_loss_coef` option)
- **If you want to use CRF layer, give `--use_crf` option**
## Dependencies
- python>=3.6
- torch==1.6.0
- transformers==3.0.2
- seqeval==0.0.12
- pytorch-crf==0.7.2
## Dataset
| | Train | Dev | Test | Intent Labels | Slot Labels |
| ----- | ------ | --- | ---- | ------------- | ----------- |
| ATIS | 4,478 | 500 | 893 | 21 | 120 |
| Snips | 13,084 | 700 | 700 | 7 | 72 |
- The number of labels are based on the _train_ dataset.
- Add `UNK` for labels (For intent and slot labels which are only shown in _dev_ and _test_ dataset)
- Add `PAD` for slot label
## Training & Evaluation
```bash
$ python3 main.py --task {task_name} \
--model_type {model_type} \
--model_dir {model_dir_name} \
--do_train --do_eval \
--use_crf
# For ATIS
$ python3 main.py --task atis \
--model_type bert \
--model_dir atis_model \
--do_train --do_eval
# For Snips
$ python3 main.py --task snips \
--model_type bert \
--model_dir snips_model \
--do_train --do_eval
```
## Prediction
```bash
$ python3 predict.py --input_file {INPUT_FILE_PATH} --output_file {OUTPUT_FILE_PATH} --model_dir {SAVED_CKPT_PATH}
```
## Results
- Run 5 ~ 10 epochs (Record the best result)
- Only test with `uncased` model
- ALBERT xxlarge sometimes can't converge well for slot prediction.
| | | Intent acc (%) | Slot F1 (%) | Sentence acc (%) |
| --------- | ---------------- | -------------- | ----------- | ---------------- |
| **Snips** | BERT | **99.14** | 96.90 | 93.00 |
| | BERT + CRF | 98.57 | **97.24** | **93.57** |
| | DistilBERT | 98.00 | 96.10 | 91.00 |
| | DistilBERT + CRF | 98.57 | 96.46 | 91.85 |
| | ALBERT | 98.43 | 97.16 | 93.29 |
| | ALBERT + CRF | 99.00 | 96.55 | 92.57 |
| **ATIS** | BERT | 97.87 | 95.59 | 88.24 |
| | BERT + CRF | **97.98** | 95.93 | 88.58 |
| | DistilBERT | 97.76 | 95.50 | 87.68 |
| | DistilBERT + CRF | 97.65 | 95.89 | 88.24 |
| | ALBERT | 97.64 | 95.78 | 88.13 |
| | ALBERT + CRF | 97.42 | **96.32** | **88.69** |
## Updates
- 2019/12/03: Add DistilBert and RoBERTa result
- 2019/12/14: Add Albert (large v1) result
- 2019/12/22: Available to predict sentences
- 2019/12/26: Add Albert (xxlarge v1) result
- 2019/12/29: Add CRF option
- 2019/12/30: Available to check `sentence-level semantic frame accuracy`
- 2020/01/23: Only show the result related with uncased model
- 2020/04/03: Update with new prediction code
## References
- [Huggingface Transformers](https://github.com/huggingface/transformers)
- [pytorch-crf](https://github.com/kmkurn/pytorch-crf)