
# mBART fine-tuned model for Czech abstractive summarization (AT2H-S)
This model is a fine-tuned checkpoint of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ``Abstract + Text to Headline`` (AT2H) which consists in generating a one- or two-sentence summary considered as a headline from a Czech news text.

## Dataset
The model has been trained on the [SumeCzech](https://ufal.mff.cuni.cz/sumeczech) dataset. The dataset includes around 1M Czech news-based documents consisting of a Headline, Abstract, and Full-text sections. Truncation and padding were configured for 512 tokens for the encoder and 64 for the decoder.

## Training
The model has been trained on 1x NVIDIA Tesla A100 40GB for 40 hours. During training, the model has seen 2576K documents corresponding to roughly 3 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-at2h-s"),
        ("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", 64),
            ("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"])
```
{"language": ["cs", "cs"], "license": "apache-2.0", "tags": ["abstractive summarization", "mbart-cc25", "Czech"], "datasets": ["SumeCzech dataset news-based"], "metrics": ["rouge", "rougeraw"]}
transformers pytorch mbart text2text-generation abstractive summarization mbart-cc25 Czech cs license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mbart #text2text-generation #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# mBART fine-tuned model for Czech abstractive summarization (AT2H-S)
This model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries.
## Task
The model deals with the task ''Abstract + Text to Headline'' (AT2H) which consists in generating a one- or two-sentence summary considered as a headline from a Czech news text.

## Dataset
The model has been trained on the SumeCzech dataset. The dataset includes around 1M Czech news-based documents consisting of a Headline, Abstract, and Full-text sections. Truncation and padding were configured for 512 tokens for the encoder and 64 for the decoder.

## Training
The model has been trained on 1x NVIDIA Tesla A100 40GB for 40 hours. During training, the model has seen 2576K documents corresponding to roughly 3 epochs.

# Use
Assuming you are using the provided URL file.

# mBART fine-tuned model for Czech abstractive summarization (AT2H-S)
This model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries. ## Task
The model deals with the task ''Abstract + Text to Headline'' (AT2H) which consists in generating a one- or two-sentence summary considered as a headline from a Czech news text. ## Dataset
The model has been trained on the SumeCzech dataset. The dataset includes around 1M Czech news-based documents consisting of a Headline, Abstract, and Full-text sections. Truncation and padding were configured for 512 tokens for the encoder and 64 for the decoder. ## Training
The model has been trained on 1x NVIDIA Tesla A100 40GB for 40 hours. During training, the model has seen 2576K documents corresponding to roughly 3 epochs. # Use
Assuming you are using the provided URL file.
TAGS
#transformers #pytorch #mbart #text2text-generation #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # mBART fine-tuned model for Czech abstractive summarization (AT2H-S)
This model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries. ## Task
The model deals with the task ''Abstract + Text to Headline'' (AT2H) which consists in generating a one- or two-sentence summary considered as a headline from a Czech news text. ## Dataset
The model has been trained on the SumeCzech dataset. The dataset includes around 1M Czech news-based documents consisting of a Headline, Abstract, and Full-text sections. Truncation and padding were configured for 512 tokens for the encoder and 64 for the decoder. ## Training
The model has been trained on 1x NVIDIA Tesla A100 40GB for 40 hours. During training, the model has seen 2576K documents corresponding to roughly 3 epochs. # Use
Assuming you are using the provided URL file.
TAGS
#transformers #pytorch #mbart #text2text-generation #abstractive summarization #mbart-cc25 #Czech #cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# mBART fine-tuned model for Czech abstractive summarization (AT2H-S)
This model is a fine-tuned checkpoint of facebook/mbart-large-cc25 on the Czech news dataset to produce Czech abstractive summaries.## Task
The model deals with the task ''Abstract + Text to Headline'' (AT2H) which consists in generating a one- or two-sentence summary considered as a headline from a Czech news text.## Dataset
The model has been trained on the SumeCzech dataset. The dataset includes around 1M Czech news-based documents consisting of a Headline, Abstract, and Full-text sections. Truncation and padding were configured for 512 tokens for the encoder and 64 for the decoder.## Training
The model has been trained on 1x NVIDIA Tesla A100 40GB for 40 hours. During training, the model has seen 2576K documents corresponding to roughly 3 epochs.# Use
Assuming you are using the provided URL file.