Update README.md

README.md

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----
-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+Hugging Face's logo Hugging Face
 
+Models
+Datasets
+Spaces
+Posts
+Docs
+Enterprise
+Pricing
 
+BeardedMonster
+/
+SabiYarn-125M
+Text Generation
+Transformers
+Safetensors
+nanogpt-j
+custom_code
 
-## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+library_name: transformers
+tags: []
+---
 
-### Model Sources [optional]
+# SabiYarn
 
-<!-- Provide the basic links for the model. -->
+## Model Details
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Model Description
 
-## Uses
+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.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+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.
 
-### Direct Use
+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 section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This is the smallest version, with 125M parameters. 
 
-[More Information Needed]
+- **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.
 
-### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+##
 
-[More Information Needed]
+## Uses
 
-### Out-of-Scope Use
+You can use the model for text cleaning and diacritization.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+## Bias, Risks, and Limitations
 
-[More Information Needed]
+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. 
 
-## Bias, Risks, and Limitations
+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.
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+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. 
 
-[More Information Needed]
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
 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.
+<b> Use transformers version 4.41.2 for consistent results </b>
+
+```python
+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
+  ```python
+   <diacritize>.... <ibo>
+  ```
 
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
 
-[More Information Needed]