Update README.md

README.md

@@ -1 +1,155 @@
-step: in config.json
+---
+language:
+- ko
+tags:
+- pytorch
+- causal-lm
+license: apache-2.0
+
+---
+# Polyglot-Ko-5.8B
+
+## Model Description
+Polyglot-Ko is a series of large-scale Korean autoregressive language models made by the EleutherAI polyglot team.
+
+| Hyperparameter       | Value                                                                                                                                  |
+|----------------------|----------------------------------------------------------------------------------------------------------------------------------------|
+| \\(n_{parameters}\\) | 5,885,059,072                                                                                                                           |
+| \\(n_{layers}\\)     | 28                                                                                                                                     |
+| \\(d_{model}\\)      | 4096                                                                                                                                   |
+| \\(d_{ff}\\)         | 16,384                                                                                                                                   |
+| \\(n_{heads}\\)      | 16                                                                                                                                     |
+| \\(d_{head}\\)       | 256                                                                                                                                    |
+| \\(n_{ctx}\\)        | 2,048                                                                                                                                   |
+| \\(n_{vocab}\\)      | 30,003 / 30,080                                                                                                                        |
+| Positional Encoding  | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864)                                                                   |
+| RoPE Dimensions      | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
+
+The model consists of 28 transformer layers with a model dimension of 4096, and a feedforward dimension of 16384. The model
+dimension is split into 16 heads, each with a dimension of 256. Rotary Position Embedding (RoPE) is applied to 64
+dimensions of each head. The model is trained with a tokenization vocabulary of 30003.
+
+## Training data
+
+Polyglot-Ko-5.8B was trained on 863 GB of Korean language data (1.2TB before processing), a large-scale dataset curated by [TUNiB](https://tunib.ai/). The data collection process has abided by South Korean laws. This dataset was collected for the purpose of training Polyglot-Ko models, so it will not be released for public use.  
+
+| Source                              |Size (GB) | Link                                  |
+|-------------------------------------|---------|------------------------------------------|
+| Korean blog posts                   | 682.3   | -                                        |
+| Korean news dataset                 | 87.0  | -                                        |
+| Modu corpus                         | 26.4  |corpus.korean.go.kr             |
+| Korean patent dataset               | 19.0  | -                                        |
+| Korean Q & A dataset                | 18.1  | -                                        |
+| KcBert dataset                      | 12.7  | github.com/Beomi/KcBERT          |
+| Korean fiction dataset              | 6.1   | -                                        |
+| Korean online comments              | 4.2   | -                                    |
+| Korean wikipedia                    | 1.4   | ko.wikipedia.org                 |
+| Clova call                          | < 1.0   | github.com/clovaai/ClovaCall     |
+| Naver sentiment movie corpus        | < 1.0   | github.com/e9t/nsmc              |
+| Korean hate speech dataset          | < 1.0   | -                                        |
+| Open subtitles                      | < 1.0  | opus.nlpl.eu/OpenSubtitles.php   |
+| AIHub various tasks datasets        | < 1.0   |aihub.or.kr                 |
+| Standard Korean language dictionary | < 1.0  | stdict.korean.go.kr/main/main.do |
+                            
+Furthermore, in order to avoid the model memorizing and generating personally identifiable information (PII) in the training data, we masked out the following sensitive information in the pre-processing stage:
+
+* `<|acc|>` : bank account number
+* `<|rrn|>` : resident registration number
+* `<|tell|>` : phone number
+
+## Training procedure
+Polyglot-Ko-5.8B was trained for 219 billion tokens over 105,000 steps on 256 A100 GPUs with the [GPT-NeoX framework](https://github.com/EleutherAI/gpt-neox). It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token. 
+
+## How to use
+
+This model can be easily loaded using the `AutoModelForCausalLM` class:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("EleutherAI/polyglot-ko-5.8b")
+model = AutoModelForCausalLM.from_pretrained("EleutherAI/polyglot-ko-5.8b")
+```
+
+## Evaluation results
+
+We evaluate Polyglot-Ko-5.8B on [KOBEST dataset](https://arxiv.org/abs/2204.04541), a benchmark with 5 downstream tasks, against comparable models such as skt/ko-gpt-trinity-1.2B-v0.5, kakaobrain/kogpt and facebook/xglm-7.5B, using the prompts provided in the paper. 
+
+The following tables show the results when the number of few-shot examples differ. You can reproduce these results using the [polyglot branch of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/polyglot) and the following scripts. For a fair comparison, all models were run under the same conditions and using the same prompts. In the tables, `n` refers to the number of few-shot examples. 
+
+```console
+python main.py \
+   --model gpt2 \
+   --model_args pretrained='EleutherAI/polyglot-ko-5.8b' \
+   --tasks kobest_copa,kobest_hellaswag \
+   --num_fewshot $YOUR_NUM_FEWSHOT \
+   --batch_size $YOUR_BATCH_SIZE \
+   --device $YOUR_DEVICE \
+   --output_path $/path/to/output/
+```
+
+**We show model performance on COPA and HellaSwag. On the other three tasks, the evaluated models all performed similarly close to random guessing.**
+
+### COPA (F1)
+
+| Model                                                                                        | params | n=0 | n=5 | n=10 | n=50 |
+|----------------------------------------------------------------------------------------------|--------|--------|--------|---------|---------|
+| [skt/ko-gpt-trinity-1.2B-v0.5](https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5)          | 1.2B   | 0.6696 | 0.6477 | 0.6419  | 0.6514  |
+| [kakaobrain/kogpt](https://huggingface.co/kakaobrain/kogpt)                                  | 6.0B   | 0.7345 | 0.7287 | 0.7277  | 0.7479  |
+| [facebook/xglm-7.5B](https://huggingface.co/facebook/xglm-7.5B)                              | 7.5B   | 0.6723 | 0.6731 | 0.6769  | 0.7119  |
+| [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b)            | 1.3B   | 0.7196 | 0.7193 | 0.7204  | 0.7206  |
+| [EleutherAI/polyglot-ko-3.8b](https://huggingface.co/EleutherAI/polyglot-ko-3.8b)            | 3.8B   | 0.7595 | 0.7608 | 0.7638  | 0.7788  |
+| **[EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b)(this)**  |**5.8B**| **0.7745**|**0.7676**|**0.7775**|**0.7887**|
+
+<img src="https://user-images.githubusercontent.com/38183241/194697388-f0e6999d-3935-4716-9faa-e14e5a9b6de5.png" width="800px">
+
+### HellaSwag (F1)
+
+| Model                                                                                          | params |n=0 | n=5 | n=10 | n=50 |
+|------------------------------------------------------------------------------------------------|--------|--------|--------|---------|---------|
+| [skt/ko-gpt-trinity-1.2B-v0.5](https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5)            | 1.2B   | 0.4036 | 0.4    | 0.4011  | 0.4214  |
+| [kakaobrain/kogpt](https://huggingface.co/kakaobrain/kogpt)                                    | 6.0B   | 0.4599 | 0.456  | 0.4616  | 0.4754  |
+| [facebook/xglm-7.5B](https://huggingface.co/facebook/xglm-7.5B)                                | 7.5B   | 0.4261 | 0.437  | 0.4409  | 0.4517  |
+| [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b)              | 1.3B   | 0.4013 | 0.3984 | 0.417   | 0.4416  |
+| [EleutherAI/polyglot-ko-3.8b](https://huggingface.co/EleutherAI/polyglot-ko-3.8b)              | 3.8B   | 0.4438 | 0.4786 | 0.4737  | 0.4822  |
+| **[EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b)**          | **5.8B**| **0.4853** |**0.482**|**0.4968**|**0.5012**|
+
+<img src="https://user-images.githubusercontent.com/38183241/194697387-218a1ea1-0863-4ea2-b8b4-339b95bdea7f.png" width="800px">
+
+## Limitations and Biases
+
+Polyglot-Ko has been trained to optimize next token prediction. Language models such as this are often used for a wide variety of tasks and it is important to be aware of possible unexpected outcomes. For instance, Polyglot-Ko will not always return the most factual or accurate response but the most statistically likely one. In addition, Polyglot may produce socially unacceptable or offensive content. We recommend having a human curator or other filtering mechanism to censor sensitive content. 
+
+## Citation and Related Information
+### BibTeX entry
+If you find our work useful, please consider citing:
+```bibtex
+@misc{polyglot-ko,
+  title = {{Polyglot-Ko: Open-Source Korean Autoregressive Language Model}},
+  author = {Ko, Hyunwoong and Yang, Kichang and Ryu, Minho and Kim, Taekyun and Yang, Seungmu and Hyun, jiwung and Park, Sungho},
+  url = {https://www.github.com/eleutherai/polyglot},
+  month = {9},
+  year = {2022},
+}
+```
+
+### Licensing
+All our models are licensed under the terms of the Apache License 2.0.
+
+```
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+```
+
+### Acknowledgement
+
+This project was made possible thanks to the computing resources from [Stability.ai](https://stability.ai), and thanks to [TUNiB](https://tunib.ai) for providing a large-scale Korean dataset for this work.