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Model: GroNLP/gpt2-medium-dutch-embeddings
Source: Original Platform

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README.md

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+---
+language: nl
+tags:
+- adaption
+- recycled
+- gpt2-medium
+pipeline_tag: text-generation
+---
+
+# GPT-2 recycled for Dutch (medium, adapted lexical embeddings)
+[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
+[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
+
+## Model description
+
+This model is based on the medium OpenAI GPT-2 ([`gpt2-medium`](https://huggingface.co/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 a Dutch vocabulary.
+
+For details, check out our paper on [arXiv](https://arxiv.org/abs/2012.05628) and the code on [Github](https://github.com/wietsedv/gpt2-recycle).
+
+
+## Related models
+
+### Dutch
+ - [`gpt2-small-dutch-embeddings`](https://huggingface.co/GroNLP/gpt2-small-dutch-embeddings): Small model size with only retrained lexical embeddings.
+ - [`gpt2-small-dutch`](https://huggingface.co/GroNLP/gpt2-small-dutch):  Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (**Recommended**)
+ - [`gpt2-medium-dutch-embeddings`](https://huggingface.co/GroNLP/gpt2-medium-dutch-embeddings): Medium model size with only retrained lexical embeddings.
+
+### Italian
+ - [`gpt2-small-italian-embeddings`](https://huggingface.co/GroNLP/gpt2-small-italian-embeddings): Small model size with only retrained lexical embeddings.
+ - [`gpt2-small-italian`](https://huggingface.co/GroNLP/gpt2-small-italian):  Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (**Recommended**)
+ - [`gpt2-medium-italian-embeddings`](https://huggingface.co/GroNLP/gpt2-medium-italian-embeddings): Medium model size with only retrained lexical embeddings.
+
+
+## How to use
+
+```python
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="GroNLP/gpt2-medium-dutch-embeddings")
+```
+
+```python
+from transformers import AutoTokenizer, AutoModel, TFAutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("GroNLP/gpt2-medium-dutch-embeddings")
+model = AutoModel.from_pretrained("GroNLP/gpt2-medium-dutch-embeddings")  # PyTorch
+model = TFAutoModel.from_pretrained("GroNLP/gpt2-medium-dutch-embeddings")  # Tensorflow
+```
+
+## BibTeX entry
+
+```bibtex
+@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}
+}
+```

config.json

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+{
+  "_name_or_path": "data/hf/gpt2-medium-dutch-embeddings",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 1,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 1024,
+  "n_head": 16,
+  "n_inner": null,
+  "n_layer": 24,
+  "n_positions": 1024,
+  "n_special": 0,
+  "pad_token_id": 0,
+  "predict_special_tokens": true,
+  "resid_pdrop": 0.1,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 100,
+      "no_repeat_ngram_size": 4,
+      "num_beams": 10,
+      "repetition_penalty": 10.0,
+      "temperature": 2.0,
+      "top_k": 20,
+      "top_p": 0.9
+    }
+  },
+  "use_cache": true,
+  "vocab_size": 40000
+}

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