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# deberta-finetuned-answer-polarity-1e6

This model is a fine-tuned version of [microsoft/deberta-large](https://huggingface.co/microsoft/deberta-large) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7823
- F1: 0.8586

## 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: 1e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 262  | 0.7424          | 0.4877 |
| 0.8987        | 2.0   | 524  | 0.3792          | 0.8774 |
| 0.2993        | 3.0   | 786  | 0.5936          | 0.8413 |
| 0.1483        | 4.0   | 1048 | 0.4211          | 0.8859 |
| 0.1175        | 5.0   | 1310 | 0.4684          | 0.8959 |
| 0.0816        | 6.0   | 1572 | 0.6284          | 0.8712 |
| 0.0624        | 7.0   | 1834 | 0.7823          | 0.8586 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["f1"], "model-index": [{"name": "deberta-finetuned-answer-polarity-1e6", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "answer_pol", "split": "validation", "args": "answer_pol"}, "metrics": [{"type": "f1", "value": 0.8586364216686151, "name": "F1"}]}]}]}
transformers pytorch deberta text-classification generated_from_trainer dataset:glue license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

deberta-finetuned-answer-polarity-1e6
=====================================


This model is a fine-tuned version of microsoft/deberta-large on the glue dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7823
* F1: 0.8586


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: 1e-06
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 7


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-06
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 7 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #deberta #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-06
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 7 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3