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# rafsankabir/Finetuned_NLI_TeacherModel

This model is a fine-tuned version of [sagorsarker/bangla-bert-base](https://huggingface.co/sagorsarker/bangla-bert-base) on the xnli_bn dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5831
- Accuracy: 0.6879

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.8381        | 1.0   | 11921  | 0.7554          | 0.6701   |
| 0.7221        | 2.0   | 23842  | 0.7214          | 0.6833   |
| 0.643         | 3.0   | 35763  | 0.7164          | 0.6920   |
| 0.5614        | 4.0   | 47684  | 0.7536          | 0.6862   |
| 0.4761        | 5.0   | 59605  | 0.8104          | 0.6875   |
| 0.395         | 6.0   | 71526  | 0.9219          | 0.6891   |
| 0.3239        | 7.0   | 83447  | 1.0047          | 0.6833   |
| 0.2627        | 8.0   | 95368  | 1.0624          | 0.6900   |
| 0.2138        | 9.0   | 107289 | 1.2522          | 0.6714   |
| 0.1768        | 10.0  | 119210 | 1.2947          | 0.6763   |
| 0.1455        | 11.0  | 131131 | 1.4790          | 0.6838   |
| 0.1246        | 12.0  | 143052 | 1.5446          | 0.6813   |
| 0.1073        | 13.0  | 154973 | 1.7562          | 0.6742   |
| 0.094         | 14.0  | 166894 | 1.8442          | 0.6891   |
| 0.0822        | 15.0  | 178815 | 1.9902          | 0.6842   |
| 0.0707        | 16.0  | 190736 | 2.2021          | 0.6825   |
| 0.0589        | 17.0  | 202657 | 2.2803          | 0.6854   |
| 0.0497        | 18.0  | 214578 | 2.4199          | 0.6804   |
| 0.0407        | 19.0  | 226499 | 2.5014          | 0.6850   |
| 0.0337        | 20.0  | 238420 | 2.5831          | 0.6879   |


### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xnli_bn"], "metrics": ["accuracy"], "model-index": [{"name": "rafsankabir/Finetuned_NLI_TeacherModel", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "xnli_bn", "type": "xnli_bn", "config": "xnli_bn", "split": "validation", "args": "xnli_bn"}, "metrics": [{"type": "accuracy", "value": 0.6878875568416701, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:xnli_bn license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-xnli_bn #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

rafsankabir/Finetuned\_NLI\_TeacherModel
========================================


This model is a fine-tuned version of sagorsarker/bangla-bert-base on the xnli\_bn dataset.
It achieves the following results on the evaluation set:


* Loss: 2.5831
* Accuracy: 0.6879


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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.0+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.0+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-xnli_bn #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.0+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-xnli_bn #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 33
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.0+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2