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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, 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
🧠 es_otomi_model
Este modelo es una versión ajustada de facebook/mbart-large-50-many-to-many-mmt, entrenada para la traducción automática entre español y otomí (Hñähñu).
📝 Descripción del modelo
El modelo está basado en mBART50, un modelo multilingüe de traducción.
Fue ajustado (fine-tuned) para mejorar la traducción de textos entre español y otomí usando un corpus paralelo creado específicamente para esta tarea.
Tarea: text2text-generation
Librería: 🤗 transformers
Framework: PyTorch
Idiomas: Español (es) y Otomí (oto)
⚙ Parámetros de entrenamiento
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: AdamW
- scheduler: linear
- num_epochs: 5
🚀 Cómo usarlo
🔹 En Python
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
model_name = "imshelala/es_otomi_model"
tokenizer = MBart50TokenizerFast.from_pretrained(model_name)
model = MBartForConditionalGeneration.from_pretrained(model_name)
text = "Traduce al otomí: Hola, ¿cómo estás?"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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facebook/mbart-large-50-many-to-many-mmt