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--- |
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language: |
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- cs |
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- en |
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- de |
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- fr |
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- tu |
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- zh |
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- es |
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- ru |
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tags: |
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- Summarization |
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- abstractive summarization |
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- mbart-large-cc25 |
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- Czech |
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- text2text generation |
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- text generation |
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license: cc-by-sa-4.0 |
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datasets: |
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- Multilingual_large_dataset_(multilarge) |
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- cnc/dm |
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- xsum |
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- mlsum |
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- cnewsum |
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- cnc |
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- sumeczech |
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metrics: |
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- rouge |
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- rougeraw |
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- MemesCS |
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--- |
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# mbart25-multilingual-summarization-multilarge-cs |
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This model is a fine-tuned checkpoint of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the Multilingual large summarization dataset focused on Czech texts to produce multilingual summaries. |
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|
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## Task |
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The model deals with a multi-sentence summary in eight different languages. With the idea of adding other foreign language documents, and by having a considerable amount of Czech documents, we aimed to improve model summarization in the Czech language. Supported languages: 'en_XX' : 'en', 'de_DE': 'de', 'es_XX': 'es', 'fr_XX':'fr', 'ru_RU':'ru', 'tr_TR':'tr'. |
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|
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# USAGE |
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Assume that you are using the provided MultilingualSummarizer.ipynb file and included files from git repository. |
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```python |
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## Configuration of summarization pipeline |
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# |
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def summ_config(): |
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cfg = OrderedDict([ |
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|
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## summarization model - checkpoint |
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# ctu-aic/m2m100-418M-multilingual-summarization-multilarge-cs |
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# ctu-aic/mt5-base-multilingual-summarization-multilarge-cs |
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# ctu-aic/mbart25-multilingual-summarization-multilarge-cs |
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("model_name", "ctu-aic/mbart25-multilingual-summarization-multilarge-cs"), |
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|
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## language of summarization task |
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# language : string : cs, en, de, fr, es, tr, ru, zh |
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("language", "en"), |
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|
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## generation method parameters in dictionary |
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# |
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("inference_cfg", OrderedDict([ |
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("num_beams", 4), |
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("top_k", 40), |
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("top_p", 0.92), |
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("do_sample", True), |
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("temperature", 0.95), |
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("repetition_penalty", 1.23), |
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("no_repeat_ngram_size", None), |
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("early_stopping", True), |
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("max_length", 128), |
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("min_length", 10), |
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])), |
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#texts to summarize values = (list of strings, string, dataset) |
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("texts", |
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[ |
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"english text1 to summarize", |
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"english text2 to summarize", |
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] |
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), |
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#OPTIONAL: Target summaries values = (list of strings, string, None) |
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('golds', |
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[ |
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"target english text1", |
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"target english text2", |
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]), |
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#('golds', None), |
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]) |
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return cfg |
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cfg = summ_config() |
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msummarizer = MultiSummarizer(**cfg) |
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ret = msummarizer(**cfg) |
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``` |
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## Dataset |
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Multilingual large summarization dataset consists of 10 sub-datasets mainly based on news and daily mails. For the training, it was used the entire training set and 72% of the validation set. |
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``` |
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Train set: 3 464 563 docs |
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Validation set: 121 260 docs |
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``` |
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| Stats | fragment | | | avg document length | | avg summary length | | Documents | |
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|-------------|----------|---------------------|--------------------|--------|---------|--------|--------|--------| |
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| __dataset__ |__compression__ | __density__ | __coverage__ | __nsent__ | __nwords__ | __nsent__ | __nwords__ | __count__ | |
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| cnc | 7.388 | 0.303 | 0.088 | 16.121 | 316.912 | 3.272 | 46.805 | 750K | |
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| sumeczech | 11.769 | 0.471 | 0.115 | 27.857 | 415.711 | 2.765 | 38.644 | 1M | |
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| cnndm | 13.688 | 2.983 | 0.538 | 32.783 | 676.026 | 4.134 | 54.036 | 300K | |
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| xsum | 18.378 | 0.479 | 0.194 | 18.607 | 369.134 | 1.000 | 21.127 | 225K| |
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| mlsum/tu | 8.666 | 5.418 | 0.461 | 14.271 | 214.496 | 1.793 | 25.675 | 274K | |
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| mlsum/de | 24.741 | 8.235 | 0.469 | 32.544 | 539.653 | 1.951 | 23.077 | 243K| |
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| mlsum/fr | 24.388 | 2.688 | 0.424 | 24.533 | 612.080 | 1.320 | 26.93 | 425K | |
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| mlsum/es | 36.185 | 3.705 | 0.510 | 31.914 | 746.927 | 1.142 | 21.671 | 291K | |
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| mlsum/ru | 78.909 | 1.194 | 0.246 | 62.141 | 948.079 | 1.012 | 11.976 | 27K| |
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| cnewsum | 20.183 | 0.000 | 0.000 | 16.834 | 438.271 | 1.109 | 21.926 | 304K | |
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#### Tokenization |
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Truncation and padding were set to 512 tokens for the encoder (input text) and 128 for the decoder (summary). |
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## Training |
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Trained based on cross-entropy loss. |
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``` |
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Time: 3 days 8 hours |
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Epochs: 860K steps cca 8 (from 10) |
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GPUs: 4x NVIDIA A100-SXM4-40GB |
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eloss: 2.214 - 1.762 |
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tloss: 3.365 - 1.445 |
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``` |
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### ROUGE results per individual dataset test set: |
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| ROUGE | ROUGE-1 | | | ROUGE-2 | | | ROUGE-L | | | |
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|-----------|---------|---------|-----------|--------|--------|-----------|--------|--------|---------| |
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| dataset |Precision | Recall | Fscore | Precision | Recall | Fscore | Precision | Recall | Fscore | |
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| cnc | 27.45 | 24.8 | 25.24 | 9.35 | 8.54 | 8.67 | 20.14 | 18.19 | 18.54 | |
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| sumeczech | 25.38 | 21.61 | 22.66 | 7.71 | 6.67 | 6.96 | 18.76 | 16.02 | 16.78 | |
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| cnndm | 41.97 | 42.61 | 41.05 | 19.64 | 19.88 | 19.16 | 29.38 | 29.85 | 28.73 | |
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| xsum | 39.18 | 39.8 | 38.83 | 16.59 | 16.98 | 16.5 | 31.25 | 31.74 | 30.96 | |
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| mlsum-tu | 51.02 | 47.95 | 47.72 | 36.15 | 34.07 | 33.9 | 44.59 | 41.9 | 41.74 | |
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| mlsum-de | 46.96 | 46.16 | 46.02 | 35.95 | 35.87 | 35.66 | 43.26 | 42.7 | 42.53 | |
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| mlsum-fr | 34.51 | 31.4 | 32.03 | 16.56 | 15.07 | 15.37 | 26.73 | 24.41 | 24.86 | |
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| mlsum-es | 32.62 | 29.66 | 30.21 | 13.3 | 12.2 | 12.39 | 26.24 | 24.02 | 24.4 | |
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| mlsum-ru | 1.25 | 1.54 | 1.31 | 0.46 | 0.46 | 0.44 | 1.25 | 1.54 | 1.31 | |
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| cnewsum | 26.43 | 29.44 | 26.38 | 7.38 | 8.52 | 7.46 | 25.99 | 28.94 | 25.92 | |
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