
<!-- 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. -->

# DeBERTa v3 (small) fine-tuned on SST2

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2134
- Accuracy: 0.9404

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.176         | 1.0   | 4210  | 0.2134          | 0.9404   |
| 0.1254        | 2.0   | 8420  | 0.2362          | 0.9415   |
| 0.0957        | 3.0   | 12630 | 0.3187          | 0.9335   |
| 0.0673        | 4.0   | 16840 | 0.3039          | 0.9266   |
| 0.0457        | 5.0   | 21050 | 0.3521          | 0.9312   |


### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3

{"language": ["en"], "license": "mit", "tags": ["generated_from_trainer", "deberta-v3"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-small", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE SST2", "type": "glue", "args": "sst2"}, "metrics": [{"type": "accuracy", "value": 0.9403669724770642, "name": "Accuracy"}]}, {"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "sst2", "split": "validation"}, "metrics": [{"type": "accuracy", "value": 0.9403669724770642, "name": "Accuracy", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2MyOTE4ZTk0YzUyNGFkMGVjNTk4MDBlZGRlZjgzOGIzYWY0YjExMmZmMDZkYjFmOTlkYmM2ZDEwYjMxM2JkOCIsInZlcnNpb24iOjF9.Ks2vdjAFUe0isZp4F-OFK9HzvPqeU3mJEG_XJfOvkTdm9DyaefT9x78sof8i_EbIync5Ao7NOC4STCTQIUvgBw"}, {"type": "precision", "value": 0.9375, "name": "Precision", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzNiZTEwNGNlZWUwZjMxYmRjNWU0ZGQ1Njg1M2MwNTQ3YWEwN2JlNDk4OWQ4MzNkMmNhOGUwMzA0YWU3ZWZjMiIsInZlcnNpb24iOjF9.p5Gbs680U45zHoWH9YgRLmOxINR4emvc2yNe9Kt3-y_WyyCd6CAAK9ht-IyGJ7GSO5WQny-ISngJFtyFt5NqDQ"}, {"type": "recall", "value": 0.9459459459459459, "name": "Recall", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjk2MmJjMDZlZDUzM2QzMWZhMzMxNWRkYjJlYzA3MjUwMThiYWMwNmQzODE1MTMxNTdkNWVmMDhhNzJjMjg3MyIsInZlcnNpb24iOjF9.Jeu6tyhXQxMykqqFH0V-IXvyTrxAsgnYByYCOJgfj86957G5LiGdfQzDtTuGkt0XcoenXhPuueT8m5tsuJyLBA"}, {"type": "auc", "value": 0.9804217184474193, "name": "AUC", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2Q5MWU1MGMzMjEwNzY4MDkzN2Q5ZjM5MTQ2MDc5YTRkZTNmNTk2YTdhODI1ZGJlOTlkNTQ2M2Q4YTUxN2Y3OSIsInZlcnNpb24iOjF9.INkDvQhg2jfD7WEE4qHJazPYo10O4Ffc5AZz5vI8fmN01rK3sXzzydvmrmTMzYSSmLhn9sc1-ZkoWbcv81oqBA"}, {"type": "f1", "value": 0.9417040358744394, "name": "F1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWRhNjljZjk0NjY1ZjU1ZjU2ZmM5ODk1YTVkMTI0ZGY4MjI1OTFlZWJkZWMyMGYxY2I1MzRjODBkNGVlMzJkZSIsInZlcnNpb24iOjF9.kQ547NVFUxeE4vNiGzGsCvMxR1MCJTChX44ds27qQ4Rj2m1UuD2C9TLTuiu8KMvq1mH1io978dJEpOCHYq6KCQ"}, {"type": "loss", "value": 0.21338027715682983, "name": "loss", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2YyYmVhNzgxMzMyNjJiNzZkYjE1YWM5Y2ZmMTlkNjQ5MThhYjIxNTE5MmE3Y2E0ODllODMyYjAzYWI3ZWRlMSIsInZlcnNpb24iOjF9.ad9rLnOeJZbRi_QQKEBpNNBp_Bt5SHf39ZeWQOZxp7tAK9dc0OK8XOqtihoXcAWDahwuoGiiYtcFNtvueaX6DA"}]}]}]}
transformers pytorch tensorboard safetensors deberta-v2 text-classification generated_from_trainer deberta-v3 en dataset:glue license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #deberta-v2 #text-classification #generated_from_trainer #deberta-v3 #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

DeBERTa v3 (small) fine-tuned on SST2
=====================================


This model is a fine-tuned version of microsoft/deberta-v3-small on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2134
* Accuracy: 0.9404


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0


### Training results



### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0 ### Training results ### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #safetensors #deberta-v2 #text-classification #generated_from_trainer #deberta-v3 #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0 ### Training results ### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #deberta-v2 #text-classification #generated_from_trainer #deberta-v3 #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0### Training results### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3