Create README.md

README.md

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+---
+language:
+- sk
+tags:
+- Slovak GPT-J
+- pytorch
+- causal-lm
+license: gpl-3.0
+---
+
+# Slovak GPT-J-1.4B
+Slovak GPT-J-1.4B with the whopping `1,415,283,792` parameters is the latest and the largest model released in Slovak GPT-J series. Smaller variants, [Slovak GPT-J-405M](https://huggingface.co/Milos/slovak-gpt-j-405M) and [Slovak GPT-J-162M](https://huggingface.co/Milos/slovak-gpt-j-162M), are still available.
+## Model Description
+Model is based on [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax/) and has over 1.4B trainable parameters.
+
+<figure>
+
+| Hyperparameter       | Value                                                                                                                                  |
+|----------------------|----------------------------------------------------------------------------------------------------------------------------------------|
+| \\(n_{parameters}\\) | 1,415,283,792                                                                                                                          |
+| \\(n_{layers}\\)     | 24                                                                                                                                     |
+| \\(d_{model}\\)      | 2048                                                                                                                                   |
+| \\(d_{ff}\\)         | 16384                                                                                                                                  |
+| \\(n_{heads}\\)      | 16                                                                                                                                     |
+| \\(d_{head}\\)       | 256                                                                                                                                    |
+| \\(n_{ctx}\\)        | 2048                                                                                                                                   |
+| \\(n_{vocab}\\)      | 50256 (same tokenizer as GPT-2/3&dagger;)                                                                                              |
+| 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) |
+
+<p><strong>&dagger;</strong> ByteLevelBPETokenizer was trained on the same Slovak corpus.</p></figure>
+
+## Training data
+
+Slovak GPT-J models were trained on a privately collected dataset consisting of predominantly Slovak text spanning different categories, e.g. web, news articles or even biblical texts - in total, over 40GB of text data was used to train this model.
+The dataset was preprocessed and cleaned in a specific way that involves minor but a few caveats, so in order to achieve the expected performance, feel free to refer to [How to use](###How to use) section. Please, keep in mind that despite the effort to remove inappropriate corpus, the model still might generate sensitive content or leak sensitive information.
+
+## Training procedure
+
+This model was trained for a bit more than 26.5 billion tokens over 48,001 steps on TPU v3-8 pod. The cross-entropy validation loss at the last step was `2.657`.
+
+## Intended Use
+
+Same as the original GPT-J, Slovak GPT-J learns an inner representation of the language that can be used to extract features useful for downstream tasks, however, the intended use is text generation from a prompt.
+
+### How to use
+
+This model along with the tokenizer can be easily loaded using the `AutoModelForCausalLM` functionality:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Milos/slovak-gpt-j-1.4B")
+model = AutoModelForCausalLM.from_pretrained("Milos/slovak-gpt-j-1.4B")
+```
+
+When generating a prompt keep in mind these three things, and you should be good to go:
+1. Never leave trailing whitespaces. There's a difference between how tokenizer encodes "Mám rád slovenčinu" (no space after `slovenčinu`) and "Mám rád slovenčinu " (trailing space after `slovenčinu`), i.e `[12805, 2872, 46878]` != `[12805, 2872, 46878, 221]`.
+2. Always use good ol' US English primary double quotation marks, i.e. `""` instead of `„“`.
+3. In case of a new line always enter `\n\n` instead of a single `\n`
+
+To illustrate an example of a basic text generation:
+```
+>>> prompt = "Tradičné jedlo na Orave sú"
+>>> encoded_input = tokenizer(prompt, return_tensors='pt')
+>>> output = model.generate(**encoded_input)
+>>> tokenizer.decode(output[0])
+'Tradičné jedlo na Orave sú bryndzové halušky\n\nNa Orave sa v minulosti varilo viac druhov'
+```
+
+### Capabilities, Limitations, and Biases
+
+Based on the parameter count and our internal testing this should be by far the most capable model out of all models released in Slovak GPT-J series.
+For sake of simplicity, I have omitted all the boilerplate code and swapped `\n` for new lines in the examples that follow. These were generated with both `top_p` and `temperature` set to `0.9`. 
+
+A spin on "Mor ho!" by Samo Chalpuka 
+```
+PROMPT
+Zleteli orly z Tatry, tiahnu na podolia, ponad vysoké hory, ponad rovné polia; preleteli cez Dunaj, cez tú šíru vodu, sadli tam za pomedzím slovenského rodu.
+
+Duní Dunaj a luna za lunou sa valí
+OUTPUT
+Zleteli orly z Tatry, tiahnu na podolia, ponad vysoké hory, ponad rovné polia; preleteli cez Dunaj, cez tú šíru vodu, sadli tam za pomedzím slovenského rodu.
+
+Duní Dunaj a luna za lunou sa valí: nad ním svieti pevný hrad na vysokom bralí. Pod tým hradom je zem slovenská, na nej je Slovensko naše.
+
+Na tom vrchu leží slovenský národ, pod tým bralom leží krajina slovenská. A pod tým hradom leží i jeho sláva: na tom vrchu, na tom brale leží sláva slovenského národa.```
+```
+
+Jokes are a bit hit or miss. Very often they contain vulgarisms and/or are highly politically incorrect. This one is rather exception than the rule.
+```
+PROMPT
+Nasledujú najlepšie vtipy.
+
+Prečo
+OUTPUT
+Nasledujú najlepšie vtipy.
+
+Prečo má policajt pri sebe vždy dva nože? Jeden je na sebaobranu, druhý na boj zblízka.
+```
+
+The infamous unicorn prompt from GPT-2 paper.
+```
+PROMPT
+V šokujúcom náleze vedci objavili stádo jednorožcov žijúcich v odľahlom, predtým nepreskúmanom údolí, v Andách. Ešte prekvapivejšie pre vedcov bol fakt, že jednorožce hovorili plynule po slovensky.
+
+OUTPUT
+V šokujúcom náleze vedci objavili stádo jednorožcov žijúcich v odľahlom, predtým nepreskúmanom údolí, v Andách. Ešte prekvapivejšie pre vedcov bol fakt, že jednorožce hovorili plynule po slovensky.
+
+V Andách ich totiž žilo približne 700 jedincov, ktoré sa živili výhradne materským mliekom. Slováci sú tak pravdepodobne prvými Európanmi, ktorí v Andách stretli jednorožca. "Je to dôkaz, že tieto zvieratá sú inteligentné a že žijú v Andách už stovky rokov," povedal pre webový portál televízie JOJ profesor geológie, geografie a zoológie, Milan Kováč.
+
+Podľa profesora Kováča si v Andách zvieratá vytvárajú svoj vlastný jazyk. Je to zároveň dôkaz, že jednorožce žili v minulosti aj v slovenských pohoriach. "Jednorožce sa tam síce vyskytovali, ale neboli tak dobre preskúmané, ako teraz v Andách."
+
+Na Slovensku však ľudia o jednorožcoch donedávna vedeli veľmi málo.<|endoftext|>
+```
+
+Since the dataset contains profanity, politically incorrect language, and (unintentionally) even a bits of text in Czech, the model can generate them in some extent too. Here's an example of the model output when prompt is in Czech:
+```
+>>> prompt = "Věta nesmí být sprostá a musí být zcela"
+>>> encoded_input = tokenizer(prompt, return_tensors='pt')
+>>> output = model.generate(**encoded_input, max_length=16)
+>>> tokenizer.decode(output[0])
+'Věta nesmí být sprostá a musí být zcela pravdivá.'
+```
+
+## Citation and Related Information
+
+This was done as a moonlighting project during summer of 2021 to better understand transformers. I didn't have much free to open source it properly, so it all sat on my hard drive until now :) Based on the popularity and interest in this model I might release _substantially_ larger versions of Slovak GPT-J models that are way more capable.
+
+If you use this model or have any questions about it feel free to hit me up at [twitter](https://twitter.com/miloskondela) or check out my [github](https://github.com/kondela) profile.
+
+### BibTeX entry
+To cite this model:
+```bibtex
+@misc{slovak-gpt-j-1.4B,
+  author = {Kondela, Milos},
+  title = {{Slovak GPT-J-1.4B}},
+  howpublished = {\url{https://huggingface.co/Milos/slovak-gpt-j-1.4B}},
+  year = 2022,
+  month = February
+}
+```
+
+To cite the codebase that trained this model:
+```bibtex
+@misc{mesh-transformer-jax,
+  author = {Wang, Ben},
+  title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}},
+  howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
+  year = 2021,
+  month = May
+}
+```
+
+## Acknowledgements
+This project was generously supported by [TPU Research Cloud (TRC) program](https://sites.research.google/trc/about/). Shoutout also goes to [Ben Wang](https://github.com/kingoflolz) and great [EleutherAI community](https://www.eleuther.ai/).