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Model: flax-community/norsk-gpt-wiki
Source: Original Platform

.gitattributes

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+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
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+model.safetensors filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language: no
+widget:
+- text: "Det er flott"
+---
+
+# GPT2-svenska-wikipedia
+A norwegian GPT2 style model trained using Flax CLM pipeline on the Norwegian
+part of the wiki40b dataset.
+
+https://huggingface.co/datasets/wiki40b
+
+## Model series
+This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
+
+## Gpt models
+
+## Swedish Gpt
+https://huggingface.co/birgermoell/swedish-gpt/
+
+## Swedish gpt wiki
+https://huggingface.co/flax-community/swe-gpt-wiki
+
+# Nordic gpt wiki
+https://huggingface.co/flax-community/nordic-gpt-wiki
+
+## Dansk gpt wiki
+https://huggingface.co/flax-community/dansk-gpt-wiki
+
+## Norsk gpt wiki
+https://huggingface.co/flax-community/norsk-gpt-wiki
+
+## Roberta models
+
+## Nordic Roberta Wiki
+https://huggingface.co/flax-community/nordic-roberta-wiki
+
+## Swe Roberta Wiki Oscar
+https://huggingface.co/flax-community/swe-roberta-wiki-oscar
+
+## Roberta Swedish Scandi
+https://huggingface.co/birgermoell/roberta-swedish-scandi
+
+## Roberta Swedish
+https://huggingface.co/birgermoell/roberta-swedish
+
+## Swedish T5 model
+https://huggingface.co/birgermoell/t5-base-swedish
+
+
+
+
+## Data cleaning and preprocessing
+The data was cleaned and preprocessed using the following script. Make sure to install depencies for beam_runner to make the dataset work.
+
+```python
+from datasets import load_dataset
+def load_and_clean_wiki():
+    dataset = load_dataset('wiki40b', 'no', beam_runner='DirectRunner', split="train")
+    #dataset = load_dataset('wiki40b', 'sv', beam_runner='DirectRunner')
+    dataset = dataset.remove_columns(['wikidata_id', 'version_id'])
+    filtered_dataset = dataset.map(filter_wikipedia)
+    # filtered_dataset[:3]
+    # print(filtered_dataset[:3])
+    return filtered_dataset
+
+def filter_wikipedia(batch):
+    batch["text"] = " ".join(batch["text"].split("\
+_START_SECTION_\
+"))
+    batch["text"] = " ".join(batch["text"].split("\
+_START_ARTICLE_\
+"))
+    batch["text"] = " ".join(batch["text"].split("\
+_START_ARTICLE_\
+"))
+    batch["text"] = " ".join(batch["text"].split("\
+_START_PARAGRAPH_\
+"))
+    batch["text"] = " ".join(batch["text"].split("_NEWLINE_"))
+    batch["text"] = " ".join(batch["text"].split("\xa0"))
+    return batch
+```
+
+## Training script
+The following training script was used to train the model.
+```bash
+./run_clm_flax.py     --output_dir="${MODEL_DIR}"     --model_type="gpt2"     --config_name="${MODEL_DIR}"     --tokenizer_name="${MODEL_DIR}"     --dataset_name="wiki40b"     --dataset_config_name="no"     --do_train --do_eval     --block_size="512"     --per_device_train_batch_size="64"     --per_device_eval_batch_size="64"     --learning_rate="5e-3" --warmup_steps="1000"     --adam_beta1="0.9" --adam_beta2="0.98" --weight_decay="0.01"     --overwrite_output_dir     --num_train_epochs="20"     --logging_steps="500"     --save_steps="1000"     --eval_steps="2500"     --push_to_hub
+```
+

added_tokens.json

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+{"<|endoftext|>": 50265}

config.json

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+{
+  "_name_or_path": ".",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.0,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 50256,
+  "gradient_checkpointing": false,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "resid_pdrop": 0.0,
+  "scale_attn_weights": true,
+  "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": 50
+    }
+  },
+  "transformers_version": "4.8.2",
+  "use_cache": true,
+  "vocab_size": 50257
+}

evaluate.py

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+from transformers import GPT2Tokenizer, GPT2Model, FlaxGPT2LMHeadModel, GPT2LMHeadModel, pipeline, set_seed
+ 
+tokenizer = GPT2Tokenizer.from_pretrained("flax-community/norsk-gpt-wiki")
+model = GPT2LMHeadModel.from_pretrained("flax-community/norsk-gpt-wiki")
+
+
+generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+set_seed(42)
+result = generator("Det er flott", max_length=150, num_return_sequences=5)
+print(result)

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make_config.py

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+from transformers import GPT2Config
+
+model_dir = "./swe-gpt-wiki" # ${MODEL_DIR}
+
+config = GPT2Config.from_pretrained("gpt2", resid_pdrop=0.0, embd_pdrop=0.0, attn_pdrop=0.0)
+config.save_pretrained(model_dir)

model.safetensors

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save_model.py

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+from transformers import AutoTokenizer, GPT2LMHeadModel
+'''
+This is a script to convert the Jax model and the tokenizer to Pytorch model
+'''
+model = GPT2LMHeadModel.from_pretrained(".", from_flax=True)
+model.save_pretrained(".")
+tokenizer = AutoTokenizer.from_pretrained(".")
+tokenizer.save_pretrained(".")

special_tokens_map.json

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+{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}

tokenizer_config.json

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+{"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "add_prefix_space": false, "special_tokens_map_file": null, "name_or_path": ".", "tokenizer_class": "GPT2Tokenizer"}