transformers tensorboard safetensors m2m_100 text2text-generation generated_from_trainer translation dataset:sarch7040/Deshika base_model:facebook/m2m100_418M base_model:finetune:facebook/m2m100_418M doi:10.57967/hf/3305 license:mit autotrain_compatible endpoints_compatible region:us
---
base_model: facebook/m2m100_418M
datasets:
- sarch7040/Deshika
library_name: transformers
license: mit
metrics:
- bleu
- meteor
pipeline_tag: translation
tags:
- generated_from_trainer
new_version: sarch7040/praTranv2
model-index:
- name: M2M101
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# M2M101

This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6766
- Bleu: 15.3416
- Meteor: 0.4723
- Gen Len: 28.0271

## Model description

This model translates Maharashtri Prakrit (an ancient Indo-Aryan Language) into English. It is fine-tuned on the M2M100 model.

## Intended uses & limitations

The use of this model is educational and is intended for students, linguists and casual learners. This model is currently limited and sometimes could give incorrect translation because of small dataset and low resources ness of Maharashtri Prakrit

## Training and evaluation data
This model was trained on Custom made dataset of 1474 Prakrit to English sentences.
The evaluation of this model was done using BLEU and METEOR score.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu    | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| No log        | 1.0   | 74   | 5.8261          | 1.1786  | 0.1997 | 36.3831 |
| No log        | 2.0   | 148  | 4.6170          | 2.648   | 0.2657 | 37.6068 |
| No log        | 3.0   | 222  | 3.5128          | 5.7069  | 0.3217 | 32.2169 |
| No log        | 4.0   | 296  | 2.5281          | 6.3134  | 0.3547 | 31.8576 |
| No log        | 5.0   | 370  | 1.7177          | 8.5036  | 0.38   | 29.9729 |
| No log        | 6.0   | 444  | 1.1666          | 10.1169 | 0.3925 | 28.0678 |
| 3.5641        | 7.0   | 518  | 0.8702          | 10.4207 | 0.4246 | 31.1051 |
| 3.5641        | 8.0   | 592  | 0.7376          | 12.6153 | 0.431  | 28.6339 |
| 3.5641        | 9.0   | 666  | 0.6901          | 13.2966 | 0.4503 | 29.2373 |
| 3.5641        | 10.0  | 740  | 0.6713          | 11.9772 | 0.4396 | 30.5661 |
| 3.5641        | 11.0  | 814  | 0.6651          | 14.0436 | 0.4506 | 30.2    |
| 3.5641        | 12.0  | 888  | 0.6678          | 13.2632 | 0.4514 | 31.0542 |
| 3.5641        | 13.0  | 962  | 0.6677          | 14.0924 | 0.4563 | 29.278  |
| 0.5121        | 14.0  | 1036 | 0.6693          | 14.746  | 0.4651 | 28.4068 |
| 0.5121        | 15.0  | 1110 | 0.6698          | 14.9278 | 0.4677 | 28.5153 |
| 0.5121        | 16.0  | 1184 | 0.6700          | 14.7431 | 0.4674 | 28.9288 |
| 0.5121        | 17.0  | 1258 | 0.6744          | 15.2934 | 0.4701 | 28.8678 |
| 0.5121        | 18.0  | 1332 | 0.6741          | 15.6776 | 0.4712 | 28.3492 |
| 0.5121        | 19.0  | 1406 | 0.6772          | 14.942  | 0.4707 | 28.9695 |
| 0.5121        | 20.0  | 1480 | 0.6766          | 15.3416 | 0.4723 | 28.0271 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0