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Model: Milos/slovak-gpt-j-1.4B Source: Original Platform
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---
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language:
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- sk
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tags:
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- Slovak GPT-J
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- pytorch
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- causal-lm
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license: gpl-3.0
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---
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# Slovak GPT-J-1.4B
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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.
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## Model Description
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Model is based on [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax/) and has over 1.4B trainable parameters.
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<figure>
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| Hyperparameter | Value |
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|----------------------|----------------------------------------------------------------------------------------------------------------------------------------|
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| \\(n_{parameters}\\) | 1,415,283,792 |
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| \\(n_{layers}\\) | 24 |
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| \\(d_{model}\\) | 2048 |
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| \\(d_{ff}\\) | 16384 |
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| \\(n_{heads}\\) | 16 |
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| \\(d_{head}\\) | 256 |
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| \\(n_{ctx}\\) | 2048 |
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| \\(n_{vocab}\\) | 50256 (same tokenizer as GPT-2/3†) |
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| Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) |
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| RoPE Dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
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<p><strong>†</strong> ByteLevelBPETokenizer was trained on the same Slovak corpus.</p></figure>
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## Training data
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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.
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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] section. Please, keep in mind that despite the effort to remove inappropriate corpus, the model still might generate sensitive content or leak sensitive information.
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## Training procedure
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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`.
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## Intended Use
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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.
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### How to use
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This model along with the tokenizer can be easily loaded using the `AutoModelForCausalLM` functionality:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("Milos/slovak-gpt-j-1.4B")
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model = AutoModelForCausalLM.from_pretrained("Milos/slovak-gpt-j-1.4B")
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```
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When generating a prompt keep in mind these three things, and you should be good to go:
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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]`.
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2. Always use good ol' US English primary double quotation marks, i.e. `""` instead of `„“`.
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3. In case of a new line always enter `\n\n` instead of a single `\n`
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To illustrate an example of a basic text generation:
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```
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>>> prompt = "Tradičné jedlo na Orave sú"
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>>> encoded_input = tokenizer(prompt, return_tensors='pt')
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>>> output = model.generate(**encoded_input)
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>>> tokenizer.decode(output[0])
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'Tradičné jedlo na Orave sú bryndzové halušky\n\nNa Orave sa v minulosti varilo viac druhov'
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```
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### Capabilities, Limitations, and Biases
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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.
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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`.
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A spin on "Mor ho!" by Samo Chalpuka
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```
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PROMPT
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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.
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Duní Dunaj a luna za lunou sa valí
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OUTPUT
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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.
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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.
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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.```
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```
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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.
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```
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PROMPT
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Nasledujú najlepšie vtipy.
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Prečo
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OUTPUT
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Nasledujú najlepšie vtipy.
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Prečo má policajt pri sebe vždy dva nože? Jeden je na sebaobranu, druhý na boj zblízka.
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```
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The infamous unicorn prompt from GPT-2 paper.
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```
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PROMPT
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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.
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OUTPUT
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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.
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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.
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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."
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Na Slovensku však ľudia o jednorožcoch donedávna vedeli veľmi málo.<|endoftext|>
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```
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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:
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```
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>>> prompt = "Věta nesmí být sprostá a musí být zcela"
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>>> encoded_input = tokenizer(prompt, return_tensors='pt')
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>>> output = model.generate(**encoded_input, max_length=16)
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>>> tokenizer.decode(output[0])
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'Věta nesmí být sprostá a musí být zcela pravdivá.'
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```
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## Citation and Related Information
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This was done as a moonlighting project during summer of 2021 to better understand transformers. I didn't have much free time to open source it properly, so it all sat on my hard drive until now :)
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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.
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### BibTeX entry
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To cite this model:
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```bibtex
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@misc{slovak-gpt-j-1.4B,
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author = {Kondela, Milos},
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title = {{Slovak GPT-J-1.4B}},
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howpublished = {\url{https://huggingface.co/Milos/slovak-gpt-j-1.4B}},
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year = 2022,
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month = February
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}
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```
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To cite the codebase that trained this model:
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```bibtex
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@misc{mesh-transformer-jax,
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author = {Wang, Ben},
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title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}},
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howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
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year = 2021,
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month = May
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}
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```
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## Acknowledgements
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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/).
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{
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"activation_function": "gelu_new",
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"architectures": [
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"GPTJForCausalLM"
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],
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"attn_pdrop": 0.0,
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"bos_token_id": 50256,
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"embd_pdrop": 0.0,
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"eos_token_id": 50256,
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"gradient_checkpointing": false,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gptj",
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"n_embd": 2048,
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"n_head": 16,
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"n_layer": 24,
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"n_positions": 2048,
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"rotary_dim": 64,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"transformers_version": "4.10.0.dev0",
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"tokenizer_class": "GPT2Tokenizer",
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"task_specific_params": {
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"text-generation": {
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"do_sample": true,
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"temperature": 1.0,
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"max_length": 50
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}
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},
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"torch_dtype": "float16",
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"use_cache": true,
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"vocab_size": 50256
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}
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