Create README.md

README.md

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+---
+language:
+- en
+license: apache-2.0
+datasets:
+- glue
+metrics:
+- pearsonr
+model-index:
+- name: t5-base-finetuned-stsb
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: GLUE STS-B
+      type: glue
+      args: stsb
+    metrics:
+    - name: Pearson Correlation
+      type: pearson_correlation
+      value: 0.8937
+---
+
+
+# T5-base-finetuned-stsb
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model is T5 fine-tuned on GLUE STS-B dataset. It acheives the following results on the validation set
+- Pearson Correlation Coefficient: 0.8937
+
+
+## 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 **"stsb sentence1: " + stsb_sent1 + "sentence2: " + stsb_sent2** and fed to the tokenizer to get the **input_ids** and **attention_mask**.
+Unlike other **GLUE** tasks, STS-B is a regression task where the goal is to predict a similarity score between 1 and 5. I have used the same stratey as descibed in the T5 paper for fine-tuning. In the paper, it is mentioned as 
+``` We found that most of these scores were annotated in increments of 0.2, so we simply rounded any score to the nearest increment of 0.2 and converted the result to a literal string representation of the number (e.g. the floating-point value 2.57 would be mapped to the string “2.6”). At test time, if the model outputs a string corresponding to a number between 1 and 5, we convert it to a floating-point value; otherwise, we treat the model’s prediction as incorrect. This effectively recasts the STS-B
+regression problem as a 21-class classification problem. ```
+
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-4
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: epsilon=1e-08
+- num_epochs: 3.0
+
+### Training results
+
+
+|Epoch | Training Loss | Validation Pearson Correlation Coefficient |
+|:----:|:-------------:|:-------------------:|
+|   1  |    0.8623     | 0.8200           |
+|   2  |    0.7782     | 0.8675        |
+|   3  |     0.7040   | **0.8937** |