transformers safetensors bart text-generation autotrain_compatible endpoints_compatible 4-bit bitsandbytes region:us
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Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

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bart-hindi - bnb 4bits
- Model creator: https://huggingface.co/Someman/
- Original model: https://huggingface.co/Someman/bart-hindi/




Original model description:
---
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: []
---

<!-- 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. -->

# 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

