T-TA (Transformer-based Text Auto-encoder)
This repository contains codes for Transformer-based Text Auto-encoder (T-TA, paper: Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning) using TensorFlow 2.
How to train T-TA using custom dataset
-
Prepare datasets. You need text line files.
Example:
Sentence 1. Sentence 2. Sentence 3. -
Train the sentencepiece tokenizer. You can use the
train_sentencepiece.pyor train sentencepiece model by yourself. -
Train T-TA model. Run
train.pywith customizable arguments. Here's the usage.$ python train.py --help usage: train.py [-h] [--train-data TRAIN_DATA] [--dev-data DEV_DATA] [--model-config MODEL_CONFIG] [--batch-size BATCH_SIZE] [--spm-model SPM_MODEL] [--learning-rate LEARNING_RATE] [--target-epoch TARGET_EPOCH] [--steps-per-epoch STEPS_PER_EPOCH] [--warmup-ratio WARMUP_RATIO] optional arguments: -h, --help show this help message and exit --train-data TRAIN_DATA --dev-data DEV_DATA --model-config MODEL_CONFIG --batch-size BATCH_SIZE --spm-model SPM_MODEL --learning-rate LEARNING_RATE --target-epoch TARGET_EPOCH --steps-per-epoch STEPS_PER_EPOCH --warmup-ratio WARMUP_RATIOI want to train models until the designated steps, so I added the
steps_per_epochandtarget_epocharguments. The total steps will be thesteps_per_epoch*target_epoch. -
(Optional) Test your model using KorSTS data. I trained my model with the Korean corpus, so I tested it using KorSTS data. You can evaluate KorSTS score (Spearman correlation) using
evaluate_unsupervised_korsts.py. Here's the usage.$ python evaluate_unsupervised_korsts.py --help usage: evaluate_unsupervised_korsts.py [-h] --model-weight MODEL_WEIGHT --dataset DATASET optional arguments: -h, --help show this help message and exit --model-weight MODEL_WEIGHT --dataset DATASET $ # To evaluate on dev set $ # python evaluate_unsupervised_korsts.py --model-weight ./path/to/checkpoint --dataset ./path/to/dataset/sts-dev.tsv
Training details
- Training data: lovit/namuwikitext
- Peak learning rate:
1e-4 - learning rate scheduler: Linear Warmup and Linear Decay.
- Warmup ratio: 0.05 (warmup steps: 1M * 0.05 = 50k)
- Vocab size:
15000 - num layers:
3 - intermediate size:
2048 - hidden size:
512 - attention heads:
8 - activation function:
gelu - max sequence length:
128 - tokenizer: sentencepiece
- Total steps: 1M
- Final validation accuracy of auto encoding task (ignores padding):
0.5513 - Final validation loss:
2.1691
Unsupervised KorSTS
| Model | Params | development | test |
|---|---|---|---|
| My Implementation | 17M | 65.98 | 56.75 |
| - | - | - | - |
| Korean SRoBERTa (base) | 111M | 63.34 | 48.96 |
| Korean SRoBERTa (large) | 338M | 60.15 | 51.35 |
| SXLM-R (base) | 270M | 64.27 | 45.05 |
| SXLM-R (large) | 550M | 55.00 | 39.92 |
| Korean fastText | - | - | 47.96 |
KorSTS development and test set scores (100 * Spearman Correlation). You can check the details of other models on this paper (KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding).
How to use pre-trained weight using tensorflow-hub
>>> import tensorflow as tf
>>> import tensorflow_text as text
>>> import tensorflow_hub as hub
>>> # load model
>>> model = hub.KerasLayer("https://github.com/jeongukjae/tta/releases/download/0/model.tar.gz")
>>> preprocess = hub.KerasLayer("https://github.com/jeongukjae/tta/releases/download/0/preprocess.tar.gz")
>>> # inference
>>> input_tensor = preprocess(["이 모델은 나무위키로 학습되었습니다.", "근데 이 모델 어디다가 쓸 수 있을까요?", "나는 고양이를 좋아해!", "나는 강아지를 좋아해!"])
>>> representation = model(input_tensor)
>>> representation = tf.reduce_sum(representation * tf.cast(input_tensor["input_mask"], representation.dtype)[:, :, tf.newaxis], axis=1)
>>> representation = tf.nn.l2_normalize(representation, axis=-1)
>>> similarities = tf.tensordot(representation, representation, axes=[[1], [1]])
>>> # results
>>> similarities
<tf.Tensor: shape=(4, 4), dtype=float32, numpy=
array([[0.9999999 , 0.76468784, 0.7384633 , 0.7181306 ],
[0.76468784, 1. , 0.81387675, 0.79722893],
[0.7384633 , 0.81387675, 0.9999999 , 0.96217746],
[0.7181306 , 0.79722893, 0.96217746, 1. ]], dtype=float32)>
References
- Official implementaion: https://github.com/joongbo/tta
- Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning
- KorSTS data (kakaobrain/KorNLUDatasets)
- KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding
- https://github.com/lovit/namuwikitext
짧은 영어를 뒤로 하고, 대부분의 독자분이실 한국분들을 위해 적어보자면, 단순히 "회사에서 구상중인 모델 구조가 좋을까?"를 테스트해보기 위해 개인적으로 학습해본 모델입니다. 어느정도로 잘 나오는지 궁금해서 작성한 코드이기 때문에 하이퍼 파라미터 튜닝이라던가, 데이터셋을 신중히 골랐다던가 하는 것은 없었습니다. 단지 학습해보다보니 생각보다 값이 잘 나와서 결과와 함께 공개하게 되었습니다. 커밋 로그를 보시면 짐작하실 수 있겠지만, 하루 정도에 후다닥 짜서 작은 GPU로 약 50시간 가량 돌린 모델입니다.
원 논문에 나온 값들을 최대한 따라가려 했으며, 밤에 작성했던 코드라 조금 명확하지 않은 부분이 있을 수도 있고, 원 구현과 다를 수도 있습니다. 해당 부분은 이슈로 달아주신다면 다시 확인해보겠습니다.
트러블 슈팅에 도움을 주신 백영민님(@baekyeongmin)께 감사드립니다.
922 Dec 10, 2021
186 Dec 24, 2022
20 Jul 11, 2022
6.4k Jan 1, 2023
740 Dec 24, 2022
33 Sep 22, 2022
1k Dec 26, 2022
2.3k Jan 7, 2023
10 Oct 21, 2022
12.3k Dec 31, 2022
5 Jan 03, 2022
140 Dec 06, 2022
1.1k Jan 02, 2023
4 Sep 28, 2022
91 Dec 23, 2022
641 Jan 01, 2023
135 Dec 22, 2022
167 Dec 27, 2022
7 Nov 15, 2022
8 Dec 22, 2022
14 May 24, 2022
3 Jan 03, 2023
1 Jan 28, 2022
3 Aug 25, 2022
89 Nov 10, 2022
77.2k Jan 03, 2023
97 Nov 21, 2022