Update README.md

README.md

@@ -4,45 +4,52 @@ base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
 tags:
 - generated_from_trainer
 datasets:
-- squad
+- squad_v2
 model-index:
-- name: bert-squadv2
-  results: []
+- name: bert-squadv2-biomed
+  results:
+  - task:
+      type: question-answering
+    dataset:
+      type: reading-comprehension
+      name: SQuADv2
+    metrics:
+    - name: accuracy
+      type: accuracy
+      value: 0.77
+      verified: false
+language:
+- en
+pipeline_tag: question-answering
 ---
 
-<!-- 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. -->
+# bert-squadv2-biomed
 
-# bert-squadv2
+This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the SQuADv2 dataset. It has been fine-tuned for question-answering tasks specifically related to biomedical texts, leveraging the SQuAD v2 dataset to enhance its ability to manage both answerable and unanswerable questions.
 
-This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the squad dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.1930
+## Model Description
 
-## Model description
+The base model, **PubMedBERT**, was originally pre-trained on biomedical abstracts and full-text articles from PubMed. This fine-tuned version adapts PubMedBERT for biomedical question-answering by training it with **SQuADv2**, a dataset that includes over 100,000 questions with answerable and unanswerable queries.
 
-More information needed
+- **Use Cases**: This model is particularly useful in applications where quick and accurate question-answering from biomedical literature is needed. It is designed to provide answers to specific questions, as well as to detect when no relevant answer exists.
 
-## Intended uses & limitations
+## Training and Evaluation Data
 
-More information needed
+- **Dataset**: The model was fine-tuned on the **SQuADv2** dataset, which consists of reading comprehension tasks where some questions have no answer in the provided context.
+- **Training Environment**: The model was trained in a Colab environment. A link to the training notebook can be found here: [Training Notebook](https://colab.research.google.com/drive/11je7-YnFQ-oISxC_7KS4QTfs3fgWOseU?usp=sharing).
 
-## Training and evaluation data
+## Training Procedure
 
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
+### Hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 3e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 3
+- `learning_rate`: 3e-05
+- `train_batch_size`: 16
+- `eval_batch_size`: 16
+- `seed`: 42
+- `optimizer`: Adam (betas=(0.9, 0.999), epsilon=1e-08)
+- `lr_scheduler_type`: linear
+- `num_epochs`: 3
 
 ### Training results
 
@@ -205,4 +212,4 @@ The following hyperparameters were used during training:
 - Transformers 4.34.1
 - Pytorch 2.1.0+cu118
 - Datasets 2.14.5
-- Tokenizers 0.14.1
+- Tokenizers 0.14.1