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BeardedMonster / SabiYarn-125M Text Generation Transformers Safetensors nanogpt-j custom_code

library_name: transformers tags: []

SabiYarn

Model Details

Model Description

SabiYarn-125M is the first of a series of transformer models (adopted from nanogpt and inspired by GPT-J's architecture) pretrained on a large corpus of Nigerian language data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.

More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token i only uses the inputs from 1 to i but not the future tokens. It also makes sure attention is not calculated across documents.

This way, the model learns an inner representation of the languages that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating coherent texts.

This is the smallest version, with 125M parameters.

Developed by: Aletheia.ai Research Lab
Funded by [optional]: Personal
Shared by [optional]: Jeffreypaul
Model type: GPTJX (Adopted from NanoGPT)
Language(s) (NLP): Majorly English, Yoruba, Hausa, Igbo, Pidgin and some others: Fulah/Fulfulde, Efik, Urhobo.

Uses

You can use the model for text cleaning and diacritization.

Bias, Risks, and Limitations

The training data used for this model is mostly an aggregation of data available on huggingface for nigerian languages. We know it contains a lot of unfiltered content from the internet, which is far from neutral.

Because large-scale language models of this size do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true.

Additionally, language models often reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a study of biases relevant to the intended use-case.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model. Use transformers version 4.41.2 for consistent results

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import GenerationConfig

generation_config = GenerationConfig(
    max_length=100,            # Maximum length of the generated sequence
    num_beams=5,               # Number of beams for beam search
    do_sample=True,            # Whether to use sampling instead of greedy decoding
    temperature=0.9,           # Sampling temperature
    top_k=50,                  # Top-K sampling
    top_p=0.95,                # Top-P (nucleus) sampling
    repetition_penalty=2.0,    # Repetition penalty to reduce repetitive outputs
    length_penalty=1.7,        # Length penalty to favor longer sequences
    early_stopping=True        # Stop early when all beams have finished
)

tokenizer_name = "BeardedMonster/SabiYarn-125M"
repo_name = "BeardedMonster/SabiYarn-diacritics-cleaner"
model = AutoModelForCausalLM.from_pretrained(repo_name, trust_remote_code=True)
tokenizer= AutoTokenizer.from_pretrained(tokenizer_name, trust_remote_code=True)


tasks (diacritization , text cleaning) typically have 2 tags. The first signifies the kind/type of task and the second signifies the end of the input, prompting the model to begin generation. They are as follows:

- Text Cleaning
  ```python
  <clean>... <yor>

Text Diacritization
```
 <diacritize>.... <ibo>
```