The model deals with the task Headline + Text to Abstract (HT2A) which consists in generating a multi-sentence summary considered as an abstract from a Czech news text.

Dataset

The model has been trained on the private CNC dataset provided by Czech News Center. The dataset includes 3/4M Czech news-based documents consisting of a Headline, Abstract, and Full-text sections. Truncation and padding were set to 512 tokens for the encoder and 128 for the decoder.

Training

The model has been trained on 1x NVIDIA Tesla A100 40GB for 60 hours. During training, the model has seen 3712K documents corresponding to roughly 5.5 epochs.

Use

Assuming you are using the provided Summarizer.ipynb file.

python

def summ_config():
    cfg = OrderedDict([
        # summarization model - checkpoint from website
        ("model_name", "krotima1/mbart-ht2a-c"),
        ("inference_cfg", OrderedDict([
            ("num_beams", 4),
            ("top_k", 40),
            ("top_p", 0.92),
            ("do_sample", True),
            ("temperature", 0.89),
            ("repetition_penalty", 1.2),
            ("no_repeat_ngram_size", None),
            ("early_stopping", True),
            ("max_length", 128),
            ("min_length", 10),
        ])),
        #texts to summarize
        ("text",
            [
                "Input your Czech text",
            ]
        ),
    ])
    return cfg
cfg = summ_config()
#load model    
model = AutoModelForSeq2SeqLM.from_pretrained(cfg["model_name"])
tokenizer = AutoTokenizer.from_pretrained(cfg["model_name"])
# init summarizer
summarize = Summarizer(model, tokenizer, cfg["inference_cfg"])
summarize(cfg["text"])