Spaces:
Sleeping
Sleeping
File size: 3,730 Bytes
b5dbcf3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
# JointBERT
(Unofficial) Pytorch implementation of `JointBERT`: [BERT for Joint Intent Classification and Slot Filling](https://arxiv.org/abs/1902.10909)
## Model Architecture
<p float="left" align="center">
<img width="600" src="https://user-images.githubusercontent.com/28896432/68875755-b2f92900-0746-11ea-8819-401d60e4185f.png" />
</p>
- 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)
|