Instructions to use Irisba/Mt5-neutralization-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Irisba/Mt5-neutralization-es with Transformers:

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

pipe = pipeline("text-generation", model="Irisba/Mt5-neutralization-es")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Irisba/Mt5-neutralization-es")
model = AutoModelForSeq2SeqLM.from_pretrained("Irisba/Mt5-neutralization-es")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Irisba/Mt5-neutralization-es with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Irisba/Mt5-neutralization-es"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Irisba/Mt5-neutralization-es",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Irisba/Mt5-neutralization-es

SGLang

How to use Irisba/Mt5-neutralization-es 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 "Irisba/Mt5-neutralization-es" \
    --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": "Irisba/Mt5-neutralization-es",
		"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 "Irisba/Mt5-neutralization-es" \
        --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": "Irisba/Mt5-neutralization-es",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Irisba/Mt5-neutralization-es with Docker Model Runner:
```
docker model run hf.co/Irisba/Mt5-neutralization-es
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Mt5-neutralization-es

This model is a fine-tuned version of google/mt5-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1368
Bleu: 81.3122
Gen Len: 17.4896

Intended uses & limitations

Translating Spanish sentences and texts into neutral,"inclusive" language Los alumnos: el alumnado Las enfermeras: el personal sanitario

Training and evaluation data

Training and evaluation dataset: Spanish Gender Neutralization dataset

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5.6e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
No log	1.0	440	0.1853	81.0788	18.3125
1.9771	2.0	880	0.1368	81.3122	17.4896

Framework versions

Transformers 4.50.1
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

Downloads last month: 1

Safetensors

Model size

0.6B params

Tensor type

F32

Model tree for Irisba/Mt5-neutralization-es

Base model

google/mt5-base

Finetuned

(304)

this model

Irisba
/

Mt5-neutralization-es