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# muril-base-cased-indic_glue

This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6013
- Precision: 0.8504
- Recall: 0.8595
- F1: 0.8549
- Accuracy: 0.9386

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.6533        | 0.31  | 200  | 1.3771          | 0.7055    | 0.7392 | 0.7220 | 0.8935   |
| 1.2034        | 0.62  | 400  | 1.0141          | 0.8253    | 0.8181 | 0.8217 | 0.9245   |
| 0.9116        | 0.94  | 600  | 0.7996          | 0.8442    | 0.8286 | 0.8363 | 0.9328   |
| 0.7435        | 1.25  | 800  | 0.6917          | 0.8291    | 0.8565 | 0.8426 | 0.9325   |
| 0.645         | 1.56  | 1000 | 0.6281          | 0.8494    | 0.8530 | 0.8512 | 0.9370   |
| 0.6003        | 1.88  | 1200 | 0.6013          | 0.8504    | 0.8595 | 0.8549 | 0.9386   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "muril-base-cased-indic_glue", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

muril-base-cased-indic\_glue
============================


This model is a fine-tuned version of google/muril-base-cased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6013
* Precision: 0.8504
* Recall: 0.8595
* F1: 0.8549
* Accuracy: 0.9386


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: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3