

# 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** |
{"language": ["en"], "license": "apache-2.0", "datasets": ["glue"], "metrics": ["pearsonr"], "model-index": [{"name": "t5-base-finetuned-stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE STS-B", "type": "glue", "args": "stsb"}, "metrics": [{"type": "pearson_correlation", "value": 0.8937, "name": "Pearson Correlation"}]}]}]}
transformers pytorch t5 text2text-generation en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

T5-base-finetuned-stsb
======================


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


### 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




### 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 ### 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
TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### 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 ### 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