Update README.md

README.md

@@ -1,74 +1,92 @@
+
 ---
 library_name: transformers
-license: afl-3.0
+license: apache-2.0
 base_model: masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0
 tags:
-- generated_from_trainer
+- code-switching
+- yoruba
+- african-nlp
+- language-identification
+- lid
 metrics:
 - precision
 - recall
 - f1
 - accuracy
 model-index:
-- name: yoruba-en-ner-model
-  results: []
+- name: yoruba-english-codeswitch-lid
+  results:
+  - task:
+      type: token-classification
+      name: Language Identification
+    dataset:
+      name: Yoruba-English Code-Switched Dataset
+      type: custom
+    metrics:
+    - type: f1
+      value: 0.9907
+      name: Overall F1
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Yoruba-English Code-Switching Language Identification (LID)
+
+This model is a fine-tuned version of [AfroXLM-R-Large](https://huggingface.co/masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0) designed to identify language boundaries in Yoruba-English code-switched text at the token level.
+
+## Model Description
+The model classifies each token in a sentence into one of three categories:
+- **YORUBA**: Tokens belonging to the Yoruba language.
+- **ENGLISH**: Tokens belonging to the English language.
+
+By utilizing the AfroXLM-R-Large backbone, which was pre-trained with a focus on African languages, this model demonstrates exceptional robustness in handling the morphological complexities of Yoruba and the fluid transitions in code-switched speech.
 
-# yoruba-en-ner-model
 
-This model is a fine-tuned version of [masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0](https://huggingface.co/masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0212
-- Precision: 0.9907
-- Recall: 0.9906
-- F1: 0.9907
-- Accuracy: 0.9956
 
-## Model description
+## Performance (Test Set)
+The model achieved near-perfect performance. Peak generalization was reached at **Epoch 1**. While training continued for 5 epochs for observation, the final deployed weights are from the **first epoch** to ensure maximum generalizability and prevent over-memorization of training samples.
 
-More information needed
+| Class | Precision | Recall | F1-Score | Support |
+| :--- | :--- | :--- | :--- | :--- |
+| **Overall** | **0.991** | **0.991** | **0.991** | **~80k** |
+| English | 0.995 | 0.994 | 0.994 | 63,016 |
+| Yoruba | 0.976 | 0.979 | 0.978 | 17,069 |
 
-## Intended uses & limitations
+## Intended Uses & Limitations
+### Intended Use
+- Research in Code-Switching (CS) patterns.
+- Preprocessing for Machine Translation or Speech Synthesis (TTS) involving Yoruba-English bilingual speakers.
+- Computational linguistics studies on the matrix language frame in Nigerian English.
 
-More information needed
+### Limitations
+- **Tonal Markers**: Performance may slightly vary if Yoruba text lacks standard diacritics (tonal marks).
+- **Domain Sensitivity**: Optimized for general conversational and science-related prompts; performance on archaic or highly legalistic Yoruba may vary.
 
-## Training and evaluation data
+## Training Procedure
+### Hyperparameters
+- **Base Model**: AfroXLM-R Large (550M parameters)
+- **Batch Size**: 128 (Global)
+- **Learning Rate**: 3e-05 (with Cosine Decay)
+- **Precision**: BF16 (Brain Floating Point)
+- **Optimizer**: AdamW (Fused)
 
-More information needed
+### Training Narrative
+The model converges remarkably fast due to the pre-existing linguistic knowledge in the AfroXLM-R base. Users will notice that **Validation Loss** is lowest at Epoch 1.0 ($0.0240$). Despite the training loss continuing to drop, the validation loss begins a slight upward trend thereafter, indicating that the model captures the underlying linguistic boundaries almost immediately.
 
-## Training procedure
 
-### Training hyperparameters
 
-The following hyperparameters were used during training:
-- learning_rate: 3e-05
-- train_batch_size: 64
-- eval_batch_size: 128
-- seed: 42
-- gradient_accumulation_steps: 2
-- total_train_batch_size: 128
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 5
+## How to Use
+```python
+from transformers import pipeline
 
-### Training results
+lid_model = pipeline("token-classification", model="your-username/yoruba-en-ner-model")
+text = "Egungun eleru helps to cleanse the village by carrying ebo"
+results = lid_model(text)
 
-| Training Loss | Epoch | Step | Validation Loss |
-|:-------------:|:-----:|:----:|:---------------:|
-| 0.1158        | 1.0   | 626  | 0.0240          |
-| 0.015         | 2.0   | 1252 | 0.0259          |
-| 0.0094        | 3.0   | 1878 | 0.0304          |
-| 0.0033        | 4.0   | 2504 | 0.0354          |
-| 0.002         | 5.0   | 3130 | 0.0377          |
+for entity in results:
+    print(f"Token: {entity['word']}, Language: {entity['entity']}")
 
+```
 
-### Framework versions
+## Citation
 
-- Transformers 4.57.6
-- Pytorch 2.9.0+cu126
-- Datasets 4.0.0
-- Tokenizers 0.22.2
+If you use this model in your research, please cite the Masakhane AfroXLM-R paper and this fine-tuned version.