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README.md

@@ -3,197 +3,94 @@ library_name: transformers
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-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# SabiYarn
 
+Test the whole generation capabilities here: https://huggingface.co/spaces/BeardedMonster/SabiYarn_125M
 
+Pretrained model on Nigerian languages including English using a causal language modeling (CLM) Multi-task objective. 
 
 ## 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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+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.
 
-## Uses
+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.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+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.
 
-### Direct Use
+This is the smallest version, with 125M parameters. 
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+- **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.
 
-[More Information Needed]
 
-### Downstream Use [optional]
+### Model Sources [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+- **Demo:** https://huggingface.co/spaces/BeardedMonster/SabiYarn_125M
 
-[More Information Needed]
+## Uses
 
-### Out-of-Scope Use
+You can use the raw model for text generation or fine-tune it to a downstream task.
 
-<!-- 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.
 
-[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]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import GenerationConfig
+
+generation_config = GenerationConfig(
+    max_length=100,               # Adjust this based on your translation requirements
+    max_new_tokens=50,            # Ensure sufficient tokens for your translations
+    num_beams=5,                  # Moderate number of beams for a balance between speed and quality
+    do_sample=False,              # Disable sampling to make output deterministic
+    temperature=1.0,              # Neutral temperature since sampling is off
+    top_k=0,                      # Disable top-k sampling (since sampling is off)
+    top_p=0,                      # Disable top-p (nucleus) sampling (since sampling is off)
+    repetition_penalty=4.0,       # Neutral repetition penalty for translation
+    length_penalty=3.0,           # No penalty for sequence length; modify if your translations tend to be too short/long
+    early_stopping=True           # Stop early when all beams finish to speed up generation
+)
+
+repo_name = "BeardedMonster/SabiYarn-125M-topic"
+tokenizer_name= "BeardedMonster/SabiYarn-125M"
+model = AutoModelForCausalLM.from_pretrained(repo_name, trust_remote_code=True)
+tokenizer= AutoTokenizer.from_pretrained(tokenizer_name, trust_remote_code=True)
+
+Use the following tags for the following downstream tasks:
+  ```
+- Topic classification
+  ```python
+  <classify> ...... <topic>
+  ```
+
+
+You should typically put user's input between these 2 tags. Currently, model also doesnt perform very well on NER due to the scarce data on this.
 
 ### Model Architecture and Objective
+Architecture is very similar to GPT-J
 
-[More Information Needed]
-
-### Compute Infrastructure
-
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-#### Hardware
-
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-#### Software
-
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-## 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:**
-
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-
-**APA:**
-
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-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
 
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