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# bart-hindi

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the [Someman/hindi-summarization](https://huggingface.co/datasets/Someman/hindi-summarization) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4985

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6568        | 0.14  | 500  | 0.6501          |
| 0.682         | 0.29  | 1000 | 0.5757          |
| 0.5331        | 0.43  | 1500 | 0.5530          |
| 0.5612        | 0.58  | 2000 | 0.5311          |
| 0.5685        | 0.72  | 2500 | 0.5043          |
| 0.4993        | 0.87  | 3000 | 0.4985          |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
{"license": "apache-2.0", "tags": ["generated_from_trainer", "hindi", "summarization", "seq2seq"], "datasets": ["Someman/hindi-summarization"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-hindi", "results": []}]}
transformers pytorch safetensors bart text2text-generation generated_from_trainer hindi summarization seq2seq dataset:Someman/hindi-summarization base_model:facebook/bart-base license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #generated_from_trainer #hindi #summarization #seq2seq #dataset-Someman/hindi-summarization #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bart-hindi
==========


This model is a fine-tuned version of facebook/bart-base on the Someman/hindi-summarization dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4985


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: 1
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+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: 1
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #generated_from_trainer #hindi #summarization #seq2seq #dataset-Someman/hindi-summarization #base_model-facebook/bart-base #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: 1
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3