JointBert-demo-chinese.rar

# 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)