
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4103
- Accuracy: 0.9175
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 2.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3631        | 1.0   | 49088 | 0.3129          | 0.9130   |
| 0.2267        | 2.0   | 98176 | 0.4157          | 0.9153   |


### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3

{"language": "en", "license": "mit", "tags": ["deberta-v1", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "pipeline_tag": "zero-shot-classification", "base_model": "microsoft/deberta-v3-large"}
transformers pytorch safetensors deberta-v2 text-classification deberta-v1 deberta-mnli zero-shot-classification en base_model:microsoft/deberta-v3-large license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

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


* Loss: 0.4103
* Accuracy: 0.9175


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-06
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 2.0


### Training results



### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0
* Datasets 1.15.2.dev0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-06
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 2.0 ### Training results ### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0
* Datasets 1.15.2.dev0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-06
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 2.0 ### Training results ### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0
* Datasets 1.15.2.dev0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-06
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 2.0### Training results### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0
* Datasets 1.15.2.dev0
* Tokenizers 0.10.3