Saraiki RoBERTa Base (Small Finetuned)
This is a fine-tuned version of the RoBERTa architecture optimized for the Saraiki language (skr). It has been trained on the themohal/saraiki_language_dataset to understand contextual nuances, vocabulary, and sentence structures native to Saraiki speakers using a Masked Language Modeling (MLM) objective.
Model Details
Model Description
RoBERTa (Robustly Optimized BERT Pretraining Approach) modifies the standard BERT design by removing the Next Sentence Prediction (NSP) objective and implementing dynamic masking patterns during training. This specific model is customized as a lightweight language encoder to assist with downstream NLP tasks for Saraiki, which remains an underrepresented regional language in AI ecosystems.
- Developed by: [Muhammad Farjad Ali Raza]
- Model type: Transformer Encoder (RoBERTa)
- Language(s) (NLP): Saraiki (skr)
- License: MIT
- Finetuned from model: roberta-base / xlm-roberta-base
- Primary Task: Fill-Mask / Masked Language Modeling
Model Sources
- Repository & Dataset: themohal/saraiki_language_dataset
Uses
Direct Use
You can use this model directly to predict missing or hidden words in a Saraiki sentence using the Hugging Face pipeline helper or direct class initializations.
Option 1: Using the pipeline API
from transformers import pipeline
# Initialize the fill-mask pipeline
nlp = pipeline("fill-mask", model="themohal/saraiki-roberta-base-small-finetuned3")
# Use the model to fill in a masked token (replace <mask> with your tokenizer's mask token if needed)
text = "سرائیکی ہک قدیم <mask> ہے۔"
predictions = nlp(text)
for pred in predictions:
print(f"Token: {pred['token_str']} | Score: {pred['score']:.4f} | Text: {pred['sequence']}")
Option 2: Direct Tokenizer & Model Loading
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("themohal/saraiki-roberta-base-small-finetuned3")
model = AutoModelForMaskedLM.from_pretrained("themohal/saraiki-roberta-base-small-finetuned3", device_map="auto")
Downstream Use
Once pre-trained on Saraiki syntax via masked modeling, this foundation is ideal for:
- Sentiment Analysis: Classifying public opinion or text feedback in Saraiki.
- Named Entity Recognition (NER): Extracting regional names, geographical locations, and native terms from text.
- Text Classification & Intent Detection: Routing localized text queries or structuring regional datasets.
Out-of-Scope Use
- Generative Text Creation: As an encoder-only model, this architecture is built to extract context or fill isolated blanks; it cannot cleanly generate long paragraphs or chat responses like a decoder-only LLM (such as GPT-4 or Llama).
- Machine Translation: This model interprets language context but is not configured out of the box to translate between English and Saraiki unless fine-tuned on a parallel corpus.
Bias, Risks, and Limitations
- Dataset Constraints: Token boundaries and script coverage depend strictly on the underlying
saraiki_language_dataset. Performance could skew if text structures deviate from the specific spelling conventions used during training. - Low-Resource Scarcity: Encoder models on small datasets risk over-fitting if training steps are not strictly regulated against evaluation losses.
Recommendations
When processing input data, guarantee that text strings are provided in standard Perso-Arabic Saraiki characters. For downstream tasks like text classification, freeze the lower layers of the encoder if your task-specific fine-tuning dataset is small, preserving the raw language insights captured here.