Update README.md

README.md

@@ -8,32 +8,50 @@ metrics:
 model-index:
 - name: roberta-base-qnli-finetuned
   results: []
+datasets:
+- nyu-mll/glue
+language:
+- en
+library_name: transformers
+pipeline_tag: text-classification
 ---
 
-<!-- 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. -->
 
 [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sarkarsiddhartha758/huggingface/runs/lft6vkrc)
 # roberta-base-qnli-finetuned
 
-This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [QNLI-data](https://huggingface.co/datasets/nyu-mll/glue/viewer/qnli)
 It achieves the following results on the evaluation set:
 - Loss: 0.2133
 - Accuracy: 0.9176
 
 ## Model description
 
-More information needed
+This is a finetuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base), it has been finetuned on the QNLI dataset,
+which contains "Question-Sentence" pairs, and labels them if they are an entailment of the question or not.
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended to be used with similar dataset like the qnli-dataset, or it can be easily finetuned to another downstream task.
+This model contains no limitations for use, anyone can use it.
 
 ## Training and evaluation data
 
-More information needed
+The dataset we used was [Qnli-dataset](https://huggingface.co/datasets/nyu-mll/glue/viewer/qnli),
+**information about dataset**: The Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, 
+where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). 
+The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, 
+and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer 
+to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying 
+assumptions that the answer is always present in the input and that lexical overlap is a reliable cue. source: [here](https://huggingface.co/datasets/nyu-mll/glue)
+<br>
+- Training dataset: The training split of QNLI data was used to train the finetuned version of roberta-base model, the training sample contains about 105,000 entries.
+- Evaluation dataset: The validation split of Qnli dataset was used to evaluate the performance of `roberta-base-qnli-finetuned`, evaluation split contains about 5460 rows
+of entry.
 
 ## Training procedure
+The model was finetuned on a `colab-environment`, with GPU: T4 selected as the GPU of choice. The dataset was first tokenized with an appropriate tokenizer
+(roberta's tokenizer), The training arguments are specified in the `Training-Hyperparameters` section.
 
 ### Training hyperparameters
 
@@ -64,4 +82,4 @@ The following hyperparameters were used during training:
 - Transformers 4.42.4
 - Pytorch 2.3.1+cu121
 - Datasets 2.20.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1