Create README.md

README.md

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+---
+language:
+- en
+license: apache-2.0
+datasets:
+- glue
+metrics:
+- accuracy
+model-index:
+- name: t5-base-finetuned-qnli
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: GLUE QNLI
+      type: glue
+      args: qnli
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9123
+---
+
+
+# T5-base-finetuned-qnli
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model is T5 fine-tuned on GLUE QNLI dataset. It acheives the following results on the validation set
+- Accuracy: 0.9123
+
+
+## Model Details
+T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format. 
+
+## Training procedure
+
+### Tokenization
+Since, T5 is a text-to-text model, the labels of the dataset are converted as follows:
+For each example, a sentence as been formed as **"qnli question: " + qnli_question + "sentence: " + qnli_sentence** and fed to the tokenizer to get the **input_ids** and **attention_mask**.
+For each label, label is choosen as **"equivalent"** if label is 1, else label is **"not_equivalent"** and tokenized to get **input_ids** and **attention_mask** . 
+  During training, these inputs_ids having **pad** token are replaced with -100 so that loss is not calculated for them. Then these input ids are given as labels, and above attention_mask of labels
+  is given as decoder attention mask.
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-4
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: epsilon=1e-08
+- num_epochs: 3.0
+
+### Training results
+
+
+|Epoch | Training Loss | Validation Accuracy |
+|:----:|:-------------:|:-------------------:|
+|   1  |    0.0571     |  0.8973             |
+|   2  |    0.0329     |  0.9068              |
+|   3  |    0.0133     |  0.9123              |