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Model: goldfish-models/zho_hans_1000mb
Source: Original Platform

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README.md

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+
+---
+license: apache-2.0
+language:
+- zho
+- cmn
+- chi
+datasets:
+- oscar-corpus/OSCAR-2109
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- goldfish
+- arxiv:2408.10441
+---
+
+# zho_hans_1000mb
+
+Goldfish is a suite of monolingual language models trained for 350 languages.
+This model is the <b>Chinese</b> (Han Simplified script) model trained on 1000MB of data, after accounting for an estimated byte premium of 0.94; content-matched text in Chinese takes on average 0.94x as many UTF-8 bytes to encode as English.
+The Goldfish models are trained primarily for comparability across languages and for low-resource languages; Goldfish performance for high-resource languages is not designed to be comparable with modern large language models (LLMs).
+
+Note: This language is available in Goldfish with other scripts (writing systems). See: zho_hant.
+
+Note: zho_hans is a [macrolanguage](https://iso639-3.sil.org/code_tables/639/data) code. None of its contained individual languages are included in Goldfish (for script hans).
+
+All training and hyperparameter details are in our paper, [Goldfish: Monolingual Language Models for 350 Languages (Chang et al., 2024)](https://www.arxiv.org/abs/2408.10441).
+
+Training code and sample usage: https://github.com/tylerachang/goldfish
+
+Sample usage also in this Google Colab: [link](https://colab.research.google.com/drive/1rHFpnQsyXJ32ONwCosWZ7frjOYjbGCXG?usp=sharing)
+
+## Model details:
+
+To access all Goldfish model details programmatically, see https://github.com/tylerachang/goldfish/blob/main/model_details.json.
+All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
+For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)!
+Details for this model specifically:
+
+* Architecture: gpt2
+* Parameters: 124770816
+* Maximum sequence length: 512 tokens
+* Training text data (raw): 935.97MB
+* Training text data (byte premium scaled): 1000.005MB
+* Training tokens: 206204416 (x10 epochs)
+* Vocabulary size: 50000
+* Compute cost: 1.052469149958144e+18 FLOPs or ~99.5 NVIDIA A6000 GPU hours
+
+Training datasets (percentages prior to deduplication):
+* 100.00000%: [OSCAR 2021/09](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)
+
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@article{chang-etal-2024-goldfish,
+  title={Goldfish: Monolingual Language Models for 350 Languages},
+  author={Chang, Tyler A. and Arnett, Catherine and Tu, Zhuowen and Bergen, Benjamin K.},
+  journal={Preprint},
+  year={2024},
+  url={https://www.arxiv.org/abs/2408.10441},
+}
+```

config.json

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+{
+  "_name_or_path": "gpt_base_config.json",
+  "activation_function": "gelu",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50000,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50001,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 512,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": 3072,
+  "n_layer": 12,
+  "n_positions": 512,
+  "pad_token_id": 50002,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "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,
+  "torch_dtype": "float32",
+  "transformers_version": "4.18.0",
+  "use_cache": true,
+  "vocab_size": 51200,
+  "prefix": "[CLS]"
+}

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tokenizer_config.json

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+{"do_lower_case": false, "remove_space": true, "keep_accents": true, "bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false, "__type": "AddedToken"}, "sp_model_kwargs": {}, "name_or_path": "models/1000mb/zho_hans_1000mb", "model_input_names": ["input_ids", "attention_mask"], "special_tokens_map_file": "models/1000mb/zho_hans_1000mb/special_tokens_map.json", "tokenizer_class": "AlbertTokenizer"}