Instructions to use SEBIS/legal_t5_small_trans_cs_sv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SEBIS/legal_t5_small_trans_cs_sv with Transformers:

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

pipe = pipeline("text-generation", model="SEBIS/legal_t5_small_trans_cs_sv")

# Load model directly
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("SEBIS/legal_t5_small_trans_cs_sv")
model = AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_cs_sv")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SEBIS/legal_t5_small_trans_cs_sv with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SEBIS/legal_t5_small_trans_cs_sv"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SEBIS/legal_t5_small_trans_cs_sv",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SEBIS/legal_t5_small_trans_cs_sv

SGLang

How to use SEBIS/legal_t5_small_trans_cs_sv 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 "SEBIS/legal_t5_small_trans_cs_sv" \
    --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": "SEBIS/legal_t5_small_trans_cs_sv",
		"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 "SEBIS/legal_t5_small_trans_cs_sv" \
        --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": "SEBIS/legal_t5_small_trans_cs_sv",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SEBIS/legal_t5_small_trans_cs_sv with Docker Model Runner:
```
docker model run hf.co/SEBIS/legal_t5_small_trans_cs_sv
```

YAML Metadata Error:"datasets[0]" with value "dcep europarl jrc-acquis" is not valid. If possible, use a dataset id from https://hf.co/datasets.

YAML Metadata Error:"language" must only contain lowercase characters

YAML Metadata Error:"language" with value "Cszech Swedish" is not valid. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". If you want to use BCP-47 identifiers, you can specify them in language_bcp47.

legal_t5_small_trans_cs_sv model

Model on translating legal text from Cszech to Swedish. It was first released in this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.

Model description

legal_t5_small_trans_cs_sv is based on the t5-small model and was trained on a large corpus of parallel text. This is a smaller model, which scales the baseline model of t5 down by using dmodel = 512, dff = 2,048, 8-headed attention, and only 6 layers each in the encoder and decoder. This variant has about 60 million parameters.

Intended uses & limitations

The model could be used for translation of legal texts from Cszech to Swedish.

How to use

Here is how to use this model to translate legal text from Cszech to Swedish in PyTorch:

from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_cs_sv"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_cs_sv", do_lower_case=False, 
                                            skip_special_tokens=True),
    device=0
)

cs_text = "Odborná příprava je v sektoru minimální a tradiční, postrádá specifické kurzy nebo výukové plány."

pipeline([cs_text], max_length=512)

Training data

The legal_t5_small_trans_cs_sv model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.

Training procedure

The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

Preprocessing

An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.

Pretraining

Evaluation results

When the model is used for translation test dataset, achieves the following results:

Test results :

Model	BLEU score
legal_t5_small_trans_cs_sv	47.9

BibTeX entry and citation info

Created by Ahmed Elnaggar/@Elnaggar_AI | LinkedIn

Downloads last month: 15