RoGPT2: 用于文本生成的罗马尼亚语GPT2

所有可用的模型：

有关代码和评估，请参见 GitHub 。

如何使用

# TensorFlow
from transformers import AutoTokenizer, TFAutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained('readerbench/RoGPT2-medium')
model = TFAutoModelForCausalLM.from_pretrained('readerbench/RoGPT2-medium')
inputs = tokenizer.encode("Este o zi de vara", return_tensors='tf')
text = model.generate(inputs, max_length=1024,  no_repeat_ngram_size=2)
print(tokenizer.decode(text[0]))

# PyTorch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained('readerbench/RoGPT2-medium')
model = AutoModelForCausalLM.from_pretrained('readerbench/RoGPT2-medium')
inputs = tokenizer.encode("Este o zi de vara", return_tensors='pt')
text = model.generate(inputs, max_length=1024,  no_repeat_ngram_size=2)
print(tokenizer.decode(text[0]))

训练

语料库统计

Corpus	Total size	Number of words	Number of sentences
OSCAR	11.54 GB	1745M	48.46M
Wiki-Ro	0.46 GB	68M	1.79M
Debates	0.5 GB	73M	3.61M
Books	4.37 GB	667M	37.39M
News	0.15 GB	23M	0.77M

训练统计

Version	Number of parameters	Number of epoch	Duration of an epoch	Context size	Batch size	PPL
Base	124M	15	7h	1024	72	22.96
Medium	354M	10	22h	1024	24	17.64
Large	774M	5	45h	512	16	16.77

评估

1. MOROCO

Model	Dialect	Md to Ro	Ro to Md
KRR + SK	94.06	67.59	75.47
BERT-base-ro	95.98	69.90	78.08
RoBERT-small	95.76	69.05	80.15
RoBERT-base	97.24	68.80	82.37
RoBERT-large	97.21	69.50	83.26
RoGPT2-base	96.69	69.82	77.55
RoGPT2-medium	96.42	69.77	80.51
RoGPT2-large	96.93	71.07	82.56

2. LaRoSeDa

Model	Binary: Accuracy	Binary: F1-Score	Multi-Class: Accuracy	Multi-Class: F1-Score
BERT-base-ro	98.07	97.94	-	79.61
RoDiBERT	98.40	98.31	-	83.01
RoBERT-small	97.44	97.43	89.30	84.23
RoBERT-base	98.27	98.26	90.59	86.27
RoBERT-large	98.20	98.19	90.93	86.63
RoGPT2-base	97.89	97.88	89.65	84.68
RoGPT2-medium	98.03	98.04	90.29	85.37
RoGPT2-large	98.06	98.07	90.26	84.89

3. RoSTS

Model	Spearman dev-set	Spearman test-set	Pearson dev-set	Pearson test-set
BERT-base-ro	84.26	80.86	84.59	81.59
RoDiBERT	77.07	71.47	77.13	72.25
RoBERT-small	82.06	78.06	81.66	78.49
RoBERT-base	84.93	80.39	85.03	80.39
RoBERT-large	86.25	83.15	86.58	83.76
RoGPT2-base	83.51	79.77	83.74	80.56
RoGPT2-medium	85.75	82.25	86.04	83.16
RoGPT2-large	85.70	82.64	86.14	83.46

4. WMT16

Model	Decoder method	Ro-En	En-Ro
mBART	-	38.5	38.5
OpenNMT	-	-	24.7
RoGPT2-base	Greedy	30.37	20.27
RoGPT2-base	Beam-search-4	31.26	22.31
RoGPT2-base	Beam-search-8	31.39	22.95
RoGPT2-medium	Greedy	32.48	22.18
RoGPT2-medium	Beam-search-4	34.08	24.03
RoGPT2-medium	Beam-search-8	34.16	24.13
RoGPT2-large	Greedy	33.69	23.31
RoGPT2-large	Beam-search-4	34.40	24.23
RoGPT2-large	Beam-search-8	34.51	24.32

5. XQuAD

Model	Decoder method	EM	F1-Score
BERT-base-ro	-	47.89	63.74
RoDiBERT	-	21.76	34.57
RoBERT-small	-	30.84	45.17
RoBERT-base	-	53.52	70.04
RoBERT-large	-	55.46	69.64
mBERT	-	59.9	72.7
XLM-R Large	-	69.7	83.6
RoGPT2-base	Greedy	23.69	35.97
RoGPT2-base	Beam-search-4	24.11	35.27
RoGPT2-medium	Greedy	29.66	44.74
RoGPT2-medium	Beam-search-4	31.59	45.32
RoGPT2-large	Greedy	29.74	42.98
RoGPT2-large	Beam-search-4	29.66	43.05
RoGPT2-base-en-ro	Greedy	23.86	34.27
RoGPT2-base-en-ro	Beam-search-4	25.04	34.51
RoGPT2-medium-en-ro	Greedy	27.05	39.75
RoGPT2-medium-en-ro	Beam-search-4	27.64	39.11
RoGPT2-large-en-ro	Greedy	28.40	39.79
RoGPT2-large-en-ro	Beam-search-4	28.73	39.71
RoGPT2-large-en-ro-mask	Greedy	31.34	44.71
RoGPT2-large-en-ro-mask	Beam-search-4	31.59	43.53

6. Wiki-Ro: LM

Model	PPL dev	PPL test
BERT-base-ro	29.0897	28.0043
RoGPT2-base	34.3795	33.7460
RoGPT2-medium	23.7879	23.4581
RoGPT2-large	21.7491	21.5200

7. RoGEC

Model	Decoder mothod	P	R	F 0.5
Transformer-tiny	Beam-search	53.53	26.36	44.38
Transformer-base Finetuning	Beam-search	56.05	46.19	53.76
Transformer-base Finetuning	Beam-search-LM	50.68	45.39	49.52
Transformer-base Finetuning	Beam-search-norm-LM	51.06	45.43	49.83
RoGPT2-base	Greedy	59.02	49.35	56.80
RoGPT2-base	Beam-search-4	65.23	49.26	61.26
RoGPT2-base	Beam-search-8	65.88	49.64	61.84
RoGPT2-medium	Greedy	69.97	57.94	67.18
RoGPT2-medium	Beam-search-4	72.46	57.99	69.01
RoGPT2-medium	Beam-search-8	72.24	57.69	68.77
RoGP2-large	Greedy	61.90	49.09	58.83
RoGP2-large	Beam-search-4	65.24	49.43	61.32
RoGP2-large	Beam-search-8	64.96	49.22	61.06
RoGPT2-base*	Greedy	68.67	49.60	63.77
RoGPT2-base*	Beam-search-4	71.16	50.53	65.79
RoGPT2-base*	Beam-search-8	71.68	50.65	66.18
RoGPT2-medium*	Greedy	58.21	43.32	54.47
RoGPT2-medium*	Beam-search-4	68.31	43.78	61.43
RoGPT2-medium*	Beam-search-8	68.68	43.99	61.75
RoGPT2-large*	Greedy	64.86	41.30	58.22
RoGPT2-large*	Beam-search-4	65.57	41.00	58.55
RoGPT2-large*	Beam-search-8	65.44	41.09	58.50

注意: * 模型是使用300万个人工生成的配对数据集进行训练的

致谢

研究得到了来自Google的 Cloud TPUs 的支持

如何引用

@inproceedings{niculescu2021rogpt2,
  title={RoGPT2: Romanian GPT2 for Text Generation},
  author={Niculescu, Mihai Alexandru and Ruseti, Stefan and Dascalu, Mihai},
  booktitle={2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)},
  pages={1154--1161},
  year={2021},
  organization={IEEE}
}

作者:

ReaderBench

数据集大小:

2.67 GB