GroNLP_-_gpt2-medium-italian-embeddings-gguf

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gpt2-medium-italian-embeddings - GGUF

• Model creator: https://huggingface.co/GroNLP/
• Original model: https://huggingface.co/GroNLP/gpt2-medium-italian-embeddings/

Original model description:

language: it tags:

• adaption
• recycled
• gpt2-medium pipeline_tag: text-generation

---

GPT-2 recycled for Italian (medium, adapted lexical embeddings)

Wietse de Vries • Malvina Nissim

Model description

This model is based on the medium OpenAI GPT-2 (gpt2-medium) model.

The Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for an Italian vocabulary.

For details, check out our paper on arXiv and the code on Github.

Related models

Dutch

• gpt2-small-dutch-embeddings: Small model size with only retrained lexical embeddings.
• gpt2-small-dutch: Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)
• gpt2-medium-dutch-embeddings: Medium model size with only retrained lexical embeddings.

Italian

• gpt2-small-italian-embeddings: Small model size with only retrained lexical embeddings.
• gpt2-small-italian: Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)
• gpt2-medium-italian-embeddings: Medium model size with only retrained lexical embeddings.

How to use

from transformers import pipeline

pipe = pipeline("text-generation", model="GroNLP/gpt2-medium-italian-embeddings")

from transformers import AutoTokenizer, AutoModel, TFAutoModel

tokenizer = AutoTokenizer.from_pretrained("GroNLP/gpt2-medium-italian-embeddings")
model = AutoModel.from_pretrained("GroNLP/gpt2-medium-italian-embeddings")  # PyTorch
model = TFAutoModel.from_pretrained("GroNLP/gpt2-medium-italian-embeddings")  # Tensorflow

BibTeX entry

@misc{devries2020good,
      title={As good as new. How to successfully recycle English GPT-2 to make models for other languages}, 
      author={Wietse de Vries and Malvina Nissim},
      year={2020},
      eprint={2012.05628},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}