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# hing-mbert-finetuned-code-mixed-DS

This model is a fine-tuned version of [l3cube-pune/hing-mbert](https://huggingface.co/l3cube-pune/hing-mbert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7248
- Accuracy: 0.7364
- Precision: 0.6847
- Recall: 0.7048
- F1: 0.6901

## 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: 2.7277800745684633e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6977        | 2.0   | 497  | 0.7248          | 0.7364   | 0.6847    | 0.7048 | 0.6901 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "hing-mbert-finetuned-code-mixed-DS", "results": []}]}
transformers pytorch safetensors bert text-classification generated_from_trainer license:cc-by-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 

hing-mbert-finetuned-code-mixed-DS
==================================


This model is a fine-tuned version of l3cube-pune/hing-mbert on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7248
* Accuracy: 0.7364
* Precision: 0.6847
* Recall: 0.7048
* F1: 0.6901


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: 2.7277800745684633e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 43
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.1+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2.7277800745684633e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 43
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.1+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2.7277800745684633e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 43
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.1+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2.7277800745684633e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 43
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.10.1+cu111
* Datasets 2.3.2
* Tokenizers 0.12.1