Update README.md

README.md

@@ -10,13 +10,14 @@ metrics:
 library_name: transformers
 tags:
 - offensive language detection
+base_model:
+- UBC-NLP/MARBERT
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model is part of the work done in <!-- add paper name -->. <br>
+The full code can be found at <!-- github repo url -->
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
 
 ## Model Details
 
@@ -26,70 +27,68 @@ This modelcard aims to be a base template for new models. It has been generated
 
 - **Model type:** BERT-based
 - **Language(s) (NLP):** Arabic
-- **License:** [More Information Needed]
 - **Finetuned from model:** UBC-NLP/MARBERT
 
-## Training Details
+## How to Get Started with the Model
 
-### Training Data
+Use the code below to get started with the model.
 
-<!-- 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. -->
+```python
+# Use a pipeline as a high-level helper
+from transformers import pipeline
 
-[More Information Needed]
+pipe = pipeline("text-classification", model="wetey/MARBERT-LHSAB")
 
-### Training Procedure
+```
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+```python
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-#### Preprocessing [optional]
+tokenizer = AutoTokenizer.from_pretrained("wetey/MARBERT-LHSAB")
+model = AutoModelForSequenceClassification.from_pretrained("wetey/MARBERT-LHSAB")
 
-[More Information Needed]
+```
 
+## Fine-tuning Details
 
-#### Training Hyperparameters
+### Fine-tuning Data
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+This model is fine-tuned on the [L-HSAB](https://github.com/Hala-Mulki/L-HSAB-First-Arabic-Levantine-HateSpeech-Dataset). The exact version we use (after removing duplicates) can be found [](). <!--TODO-->
 
-#### Speeds, Sizes, Times [optional]
+### Fine-tuning Procedure
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+The exact fine-tuning procedure followed can be found at []() <!--TODO-->
 
-[More Information Needed]
+#### Training Hyperparameters
 
+    evaluation_strategy = 'epoch'
+    logging_steps = 1,
+    num_train_epochs = 5,
+    learning_rate = 1e-5,
+    eval_accumulation_steps = 2
+    
 ## 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. -->
+### Testing Data 
 
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+  Test set used can be found at []()<!--TODO-->
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+`accuracy`: 87.9% <br>
+`precision`: 88.1% <br>
+`recall`: 87.9% <br>
+`f1-score`: 87.9% <br>
 
-## Model Card Contact
+ #### Results per class 
+| Label | Precision | Recall | F1-score|
+|---------|---------|---------|---------|
+| normal | 85% | 82% | 83% |
+| abusive | 93% | 92% | 93% |
+| hate | 68% | 78% | 72% |
 
-[More Information Needed]
+## Citation
+<!--TODO-->