Instructions to use MU-NLPC/CzeGPT-2_headline_generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MU-NLPC/CzeGPT-2_headline_generator with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MU-NLPC/CzeGPT-2_headline_generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MU-NLPC/CzeGPT-2_headline_generator")
model = AutoModelForCausalLM.from_pretrained("MU-NLPC/CzeGPT-2_headline_generator")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MU-NLPC/CzeGPT-2_headline_generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MU-NLPC/CzeGPT-2_headline_generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MU-NLPC/CzeGPT-2_headline_generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MU-NLPC/CzeGPT-2_headline_generator

SGLang

How to use MU-NLPC/CzeGPT-2_headline_generator with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MU-NLPC/CzeGPT-2_headline_generator" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MU-NLPC/CzeGPT-2_headline_generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MU-NLPC/CzeGPT-2_headline_generator" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MU-NLPC/CzeGPT-2_headline_generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MU-NLPC/CzeGPT-2_headline_generator with Docker Model Runner:
```
docker model run hf.co/MU-NLPC/CzeGPT-2_headline_generator
```

CzeGPT-2 headline generator

CzeGPT-2_headline_generator is a Czech summarizer built upon the CzeGPT-2 model. The model has the same architectural dimensions as the GPT-2 small (12 layers, 12 heads, 1024 tokens on input/output, and embedding vectors with 768 dimensions) resulting in 124M trainable parameters. It was fine-tuned and evaluated on the SumeCzech summarization dataset containing about 1M Czech news articles.

Tokenizer

Along, we also provide a Czech trained tokenizer (vocab and merges) with vocab size of 50257 that was used during the pre-training phase and fine-tuning. It is the byte-level BPE tokenizer as used in the original GPT-2 paper.

Training results

The model was evaluated on the test and ood-test partitions of the SumeCzech dataset and compared to the best summarizers yet evaluated on this benchmark (the results taken from here). The headline generator is trained to decide itself when to stop (generate an <|endoftext|> token). If you want a variable summary length, refer to our summary generator

We manage to exceed current state-of-the art on all standard metrics.

Test set

Model	ROUGE_RAW-1	ROUGE_RAW-2	ROUGE_RAW-L
CzeGPT-2	17.3/17.0/16.7	4.4/4.3/4.2	15.5/15.2/14.9
First	7.4/13.5/8.9	1.1/2.2/1.3	6.5/11.7/7.7
TextRank	6.0/16.5/8.3	0.8/2.3/1.1	5.0/13.8/6.9
Tensor2Tensor	8.8/7.0/7.5	0.8/0.6/0.7	8.1/6.5/7.0
NE Density	6.6/10.7/7.3	0.8/1.4/0.9	5.9/9.4/6.4
Seq2Seq	16.1/14.1/14.6	2.5/2.1/2.2	14.6/12.8/13.2
Seq2Seq_NER	16.2/14.1/14.7	2.5/2.1/2.2	14.7/12.8/13.3

OOD test set

Model	ROUGE_RAW-1	ROUGE_RAW-2	ROUGE_RAW-L
CzeGPT-2	17.9/17.6/17.2	5.9/5.7/5.5	16.4/16.2/15.8
First	6.7/13.6/8.3	1.3/2.8/1.6	5.9/12.0/7.4
TextRank	5.8/16.9/8.1	1.1/3.4/1.5	5.0/14.5/6.9
Tensor2Tensor	6.3/5.1/5.5	0.5/0.4/0.4	5.9/4.8/5.1
NE Density	6.3/11.4/7.1	1.3/2.3/1.4	5.7/10.2/6.3
Seq2Seq	13.1/11.8/12.0	2.0/1.7/1.8	12.1/11.0/11.2
Seq2SeqNER	16.2/14.1/14.7	2.5/2.1/2.2	14.7/12.8/13.3

The numbers in the tables denote precision/recall/F1-score

Error Analysis

As we think the current standard ROUGE_RAW metric is not suitable enough for the summarization task (even though it is the best we have at the time), we performed also a manual error analysis of the generated summaries using human annotators. You can find more about the methodology and results in our paper referenced at the bottom of this card.

Running the predictions

The repository includes a simple Jupyter Notebook that can help with first steps when using the model.

Summary generator

See also our model fine-tuned for summary generation task.

How to cite

@article{hajek_horak2024, author = "Adam Hájek and Aleš Horák", title = "CzeGPT-2 -- Training New Model for Czech Generative Text Processing Evaluated with the Summarization Task", journal= "IEEE Access", year = "2024", volume = "12", pages = "34570--34581", doi = "10.1109/ACCESS.2024.3371689", }

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Collection including MU-NLPC/CzeGPT-2_headline_generator

CzeGPT-2

Collection

A collection of small language models for Czech language generation. A general-purpose CzeGPT-2 and fine-tuned versions for abstractive summarization • 3 items • Updated Oct 13, 2024