Update README.md

README.md

@@ -1,203 +1,101 @@
 ---
 library_name: peft
 base_model: xlm-roberta-base
+license: mit
+language:
+- am
+widget:
+  - text: ኢትዮጵያ ፕሪምየር ሊግ 6ኛ ሳምንት የእሁድ ጨዋታዎች ቅድመ ዳሰሳ
+    example_title: ስፖርት (Sports)
+metrics:
+- accuracy
+- f1
+pipeline_tag: text-classification
 ---
 
-# Model Card for Model ID
+# xlm-roberta-base-lora-amharic-news-classification
 
 <!-- Provide a quick summary of what the model is/does. -->
+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 one of 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: **0.3563**
+- Validation Loss: **0.3613**
+- Validation Accuracy: **0.8642**
+- Validation F1 Score (macro): **0.8220**
+- Validation F1 Score (weighted): **0.8648**
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### 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]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[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]
-
+## How to use
 
-#### Training Hyperparameters
+You can use this model with a pipeline for text classification. 
+But first, you need to install the `peft` library like so: 
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+```console 
+pip install peft
+```
 
-#### Speeds, Sizes, Times [optional]
+Then, you can run the following code.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 
-[More Information Needed]
+model_id = "xlm-roberta-base"
+peft_model_id = "rasyosef/xlm-roberta-base-lora-amharic-news-classification" 
 
-## Evaluation
+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)}
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-### Testing Data, Factors & Metrics
+model = AutoModelForSequenceClassification.from_pretrained(
+    model_id, 
+    num_labels=len(categories), # 6
+    id2label=id2label,
+    label2id=label2id
+)
 
-#### Testing Data
+model.load_adapter(peft_model_id)
 
-<!-- This should link to a Dataset Card if possible. -->
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
 
-[More Information Needed]
+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'
+])
+```
 
-#### Factors
+Output:
+```python
+[{'label': 'ሀገር አቀፍ ዜና', 'score': 0.977573037147522}]
+```
 
-<!-- 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]
+## Model Details
 
-[More Information Needed]
+### Model Description
 
-## Model Card Contact
+<!-- Provide a longer summary of what this model is. -->
 
-[More Information Needed]
 
+- **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]
 
 ### Framework versions