Update README.md

README.md

@@ -1,64 +1,65 @@
----
-license: apache-2.0
-base_model: google/bigbird-roberta-base
+$---
+language: english
 tags:
-- generated_from_trainer
+- bigbird
+- question-answering
+- squad-v2.2
+license: apache-2.0
 datasets:
 - squad_v2
-model-index:
-- name: bigbird-QA-squad_v2.2
-  results: []
+metrics:
+- f1
+- exact_match
 ---
 
-<!-- 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. -->
-
-# bigbird-QA-squad_v2.2
-
-This model is a fine-tuned version of [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-roberta-base) on the squad_v2 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.8585
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
+# FredNajjar/bigbird-QA-squad_v2.2
+
+Fine-tuned [`google/bigbird-roberta-base`](https://huggingface.co/google/bigbird-roberta-base) model on the SQuAD 2.0 dataset for English extractive question answering.
+
+## Model Details
+- **Language Model**: [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-roberta-base)
+- **Language**: English
+- **Task**: Extractive QA
+- **Training Data**: [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/)
+- **Eval Data**: [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/)
+- **Framework Versions**:
+  - Transformers: 4.34.0
+  - Pytorch: 2.0.1+cu118
+  - Datasets: 2.14.5
+  - Tokenizers: 0.14.1
+- **Infrastructure**: 1x Tesla A100
+
+## Training Hyperparameters
+- Learning Rate: 3e-05
+- Train Batch Size: 16
+- Eval Batch Size: 8
+- Seed: 42
+- Gradient Accumulation Steps: 8
+- Total Train Batch Size: 128
+- Optimizer: Adam (betas=(0.9,0.999), epsilon=1e-08)
+- LR Scheduler: Linear with 121 warmup steps
+- Number of Epochs: 3
+
+## Results on SQuAD 2.0
+- **F1 Score**: 81.39%
+- **Exact Match**: 77.82%
+
+## Usage
+```python
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+model_name = "FredNajjar/bigbird-QA-squad_v2.2"
+nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+QA_input = {
+        'question': 'Your question here',
+    'context': 'Your context here'
+}
+res = nlp(QA_input)
+```
+
+## Limitations and Bias
+This model inherits limitations and potential biases from the base BigBird model and the SQuAD 2.0 training data.
+
+## Contact
+For inquiries, please reach out via [LinkedIn](https://www.linkedin.com/in/frednajjar/).
 
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 3e-05
-- train_batch_size: 16
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 128
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 121
-- num_epochs: 3
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss |
-|:-------------:|:-----:|:----:|:---------------:|
-| 1.0955        | 1.0   | 814  | 0.9719          |
-| 0.8505        | 2.0   | 1629 | 0.8657          |
-| 0.6993        | 3.0   | 2442 | 0.8585          |
-
-
-### Framework versions
-
-- Transformers 4.34.0
-- Pytorch 2.0.1+cu118
-- Datasets 2.14.5
-- Tokenizers 0.14.1
+---