Update README.md

README.md

@@ -3,44 +3,88 @@ library_name: transformers
 license: apache-2.0
 base_model: distilbert/distilbert-base-cased
 tags:
-- generated_from_trainer
+- question-answering
+- squadv2
+- distilbert
+- en
+- transformer
+- pytorch
 model-index:
 - name: a-question_answerer
   results: []
+language:
+- en
 ---
 
-<!-- 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. -->
-
 # a-question_answerer
 
+This model is a fine-tuned version of the DistilBERT Base Cased model on the SQuAD v2 dataset for the Question Answering task.
+
+It was trained as part of a Google Colab project aimed at adapting a pre-trained language model to answer questions based on a given text context.
+
 This model is a fine-tuned version of [distilbert/distilbert-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.8661
+- Loss:  1.3622*
 
 ## Model description
 
-More information needed
+This model is intended for use in Question Answering tasks, where the goal is to extract a concise answer span from a provided text context given a natural language question. It can handle both answerable and unanswerable questions as per the SQuAD v2 dataset format.
 
 ## Intended uses & limitations
 
-More information needed
+Potential use cases include:
+
+Building a simple document Q-A system.
+Enhancing search functionalities to provide direct answers.
+
+As with any model trained on a specific dataset, this model's performance is influenced by the characteristics and potential biases present in the SQuAD v2 dataset.
+It may perform differently on text from domains significantly different from Wikipedia articles (the source of SQuAD). 
+The model may also inherit biases from the original DistilBERT Base Cased model.
+
+The model's performance on identifying and answering questions depends heavily on the quality and relevance of the provided context.
 
 ## Training and evaluation data
 
-More information needed
+The model was fine-tuned on the SQuAD v2 dataset, which contains over 130,000 question-answer pairs derived from Wikipedia articles.
+The dataset includes questions that are unanswerable, requiring the model to determine if no answer exists within the provided text.
+
+For the final reported results, the model was trained on the full SQuAD v2 training dataset.
 
 ## Training procedure
 
+The model was fine-tuned using the Hugging Face transformers library and Trainer API.
+The training process involved tokenizing the dataset, preparing input features with start and end positions for answers, and using DataCollatorWithPadding.
+Early stopping was used to load the model checkpoint with the lowest validation loss.
+
+Training Arguments:
+
+Learning Rate: 2e-5
+Per Device Train Batch Size: 4
+Per Device Eval Batch Size: 4
+Number of Epochs: 3
+Weight Decay: 0.1
+Evaluation Strategy: epoch
+Save Strategy: epoch
+Early Stopping: Enabled (load_best_model_at_end=True, metric_for_best_model="eval_loss")
+
 ### Training hyperparameters
 
+Base Model: distilbert/distilbert-base-cased
+Dataset: SQuAD v2
+Early Stopping: Enabled (load_best_model_at_end=True, metric_for_best_model="eval_loss")
+
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
 - train_batch_size: 4
 - eval_batch_size: 4
+- weight_decay: 0.1
 - seed: 42
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
+- eval_strategy: epoch
+- save_strategy: epoch
+- load_best_model_at_end=True
+- metric_for_best_model="eval_loss"
 - num_epochs: 3
 
 ### Training results
@@ -48,13 +92,24 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step  | Validation Loss |
 |:-------------:|:-----:|:-----:|:---------------:|
 | 1.1921        | 1.0   | 32580 | 1.4150          |
-| 0.9637        | 2.0   | 65160 | 1.3622          |
+| 0.9637        | 2.0   | 65160 | 1.3622*         |
 | 0.6474        | 3.0   | 97740 | 1.8661          |
 
+## Evaluation Results
+
+The model was evaluated on the SQuAD v2 validation set. The following metrics were obtained:
+
+| Metric         | Overall   | Answerable (HasAns) | Unanswerable (NoAns) |
+|----------------|-----------|---------------------|----------------------|
+| Exact match-EM | 64.2      | 60.27               | 67.97                |
+| F1 Score       | 66.57     | 65.10               | 67.97                |
+| Total Examples | 2000      | 979                 | 1021                 |
+
+*Note: The metrics for 'Answerable' and 'Unanswerable' questions provide a more detailed view of the model's performance on each type of question in SQuAD v2.*
 
 ### Framework versions
 
 - Transformers 4.55.0
 - Pytorch 2.6.0+cu124
 - Datasets 4.0.0
-- Tokenizers 0.21.4
+- Tokenizers 0.21.4