--- library_name: peft base_model: xlm-roberta-base license: mit language: - am metrics: - accuracy - f1 pipeline_tag: text-classification --- # Model Card for Model ID This repo contains LoRA adapters for the [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) model finetuned on the [Amharic-News-Text-classification-Dataset](https://huggingface.co/datasets/israel/Amharic-News-Text-classification-Dataset). The finetuned model classifies an Amharic news article into the following 6 categories. - ሀገር አቀፍ ዜና (Local News) - መዝናኛ (Entertainment) - ስፖርት (Sports) - ቢዝነስ (Business) - ዓለም አቀፍ ዜና (International News) - ፖለቲካ (Politics) It achieves the following results on the evaluation set: - Train Loss: 0.3447 - Validation Loss: 0.3947 - Validation Accuracy: 0.8541 - Validation F1 Score (macro): 0.8105 - Validation F1 Score (weighted): 0.8551 ## How to use You can use this model with a pipeline for text classification. But first, you need to install the `peft` library like so: ```console pip install peft ``` Then, you can run the following code. ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline model_id = "xlm-roberta-base" peft_model_id = "rasyosef/xlm-roberta-base-lora-amharic-news-classification" categories = ['ሀገር አቀፍ ዜና', 'መዝናኛ', 'ስፖርት', 'ቢዝነስ', 'ዓለም አቀፍ ዜና', 'ፖለቲካ'] id2label = {i: lbl for i, lbl in enumerate(categories)} label2id = {lbl: i for i, lbl in enumerate(categories)} tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForSequenceClassification.from_pretrained( model_id, num_labels=len(categories), # 6 id2label=id2label, label2id=label2id ) model.load_adapter(peft_model_id) classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) classifier([ """ቅርሶቹን ለመታደግ የተጀመረው የሙዚዬም ግንባታም በበጀት ምክንያት ተቋርጧል። በአፄ ቴዎድሮስ የንግስና ቦታ ደረስጌ ማሪያም ተጀምሮ የቆመው የሙዚየሙ ግንባታ ተጠናቀቆ ስራ እንዲጀምር ነዋሪዎች ጠይቀዋል። ዘመነ መሳፍንት መቋጫ ያገኘባት የኢትዮጵያ አንድነት የታወጀባት ዳግማዊ አፄ ቴዎድሮስ ከመንገሳቸው በፊት ደጃች ውቤን ቧሂት ከሚባል ቦታ ድል አድርገው ደጃች ውቤ ለንግስና ባዘጋጁት የንግስና ቦታና እቃዎች ንጉሰ ነገስት ዘኢትዮጵያ ተብለው የነገሱባት ቦታ ናት።""", # 'ሀገር አቀፍ ዜና' ]) ``` Output: ```python [{'label': 'ሀገር አቀፍ ዜና', 'score': 0.977573037147522}] ``` ## Model Details ### Model Description - **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] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses ### Direct Use [More Information Needed] ### Downstream Use [optional] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### 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. [More Information Needed] ## Training Details ### Training Data [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Environmental Impact 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] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1