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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

Irisba commited on Mar 26, 2025

Commit

ffaadad

verified ·

1 Parent(s): 3a2f8d8

Update README.md

Browse files

Files changed (1) hide show

README.md +10 -9

README.md CHANGED Viewed

@@ -10,6 +10,11 @@ metrics:
 model-index:
 - name: Mt5-neutralization-es
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -23,19 +28,15 @@ It achieves the following results on the evaluation set:
 - Bleu: 81.3122
 - Gen Len: 17.4896
-## Model description
-More information needed
 ## Intended uses & limitations
-More information needed
 ## Training and evaluation data
-More information needed
-## Training procedure
 ### Training hyperparameters
@@ -61,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.50.1
 - Pytorch 2.6.0+cu124
 - Datasets 3.4.1
-- Tokenizers 0.21.1

 model-index:
 - name: Mt5-neutralization-es
   results: []
+datasets:
+- somosnlp-hackathon-2022/neutral-es
+language:
+- es
+pipeline_tag: text2text-generation
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 - 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
 - Transformers 4.50.1
 - Pytorch 2.6.0+cu124
 - Datasets 3.4.1
+- Tokenizers 0.21.1