
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# indic-mALBERT-uncased

This model is a uncased fine-tuned version of [indic-bert](https://huggingface.co/ai4bharat/indic-bert) on the squad_v2 dataset.

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 814
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Key                     | Value                  |
|-------------------------|------------------------|
| epoch                   | 5.0                    |
| eval_HasAns_exact       | 53.54251012145749      |
| eval_HasAns_f1          | 63.501893668646716     |
| eval_HasAns_total       | 5928                   |
| eval_NoAns_exact        | 75.10513036164845      |
| eval_NoAns_f1           | 75.10513036164845      |
| eval_NoAns_total        | 5945                   |
| eval_exact              | 64.3392571380443       |
| eval_f1                 | 69.31181888888594      |
| eval_runtime            | 69.5054                |
| eval_samples            | 12000                  |
| eval_samples_per_second | 172.649                |
| eval_steps_per_second   | 5.395                  |
| eval_total              | 11873                  |

### Framework versions

- Transformers 4.29.2
- Pytorch 1.9.0+cu102
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "indic-mALBERT-uncased", "results": []}]}
transformers pytorch safetensors albert question-answering generated_from_trainer dataset:squad_v2 endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #albert #question-answering #generated_from_trainer #dataset-squad_v2 #endpoints_compatible #region-us 

indic-mALBERT-uncased
=====================


This model is a uncased fine-tuned version of indic-bert on the squad\_v2 dataset.


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: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 814
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 1.9.0+cu102
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 814
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.9.0+cu102
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #safetensors #albert #question-answering #generated_from_trainer #dataset-squad_v2 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 814
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.9.0+cu102
* Datasets 2.12.0
* Tokenizers 0.13.3