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--- |
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library_name: transformers |
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license: mit |
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base_model: facebook/m2m100_418M |
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tags: |
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- generated_from_trainer |
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metrics: |
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- bleu |
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- meteor |
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model-index: |
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- name: M2M101 |
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results: [] |
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datasets: |
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- sarch7040/Deshika |
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new_version: sarch7040/praTranv2 |
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pipeline_tag: translation |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# M2M101 |
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This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6766 |
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- Bleu: 15.3416 |
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- Meteor: 0.4723 |
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- Gen Len: 28.0271 |
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## Model description |
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This model translates Maharashtri Prakrit (an ancient Indo-Aryan Language) into English. It is fine-tuned on the M2M100 model. |
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## Intended uses & limitations |
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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 |
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## Training and evaluation data |
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This model was trained on Custom made dataset of 1474 Prakrit to English sentences. |
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The evaluation of this model was done using BLEU and METEOR score. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| |
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| No log | 1.0 | 74 | 5.8261 | 1.1786 | 0.1997 | 36.3831 | |
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| No log | 2.0 | 148 | 4.6170 | 2.648 | 0.2657 | 37.6068 | |
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| No log | 3.0 | 222 | 3.5128 | 5.7069 | 0.3217 | 32.2169 | |
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| No log | 4.0 | 296 | 2.5281 | 6.3134 | 0.3547 | 31.8576 | |
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| No log | 5.0 | 370 | 1.7177 | 8.5036 | 0.38 | 29.9729 | |
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| No log | 6.0 | 444 | 1.1666 | 10.1169 | 0.3925 | 28.0678 | |
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| 3.5641 | 7.0 | 518 | 0.8702 | 10.4207 | 0.4246 | 31.1051 | |
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| 3.5641 | 8.0 | 592 | 0.7376 | 12.6153 | 0.431 | 28.6339 | |
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| 3.5641 | 9.0 | 666 | 0.6901 | 13.2966 | 0.4503 | 29.2373 | |
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| 3.5641 | 10.0 | 740 | 0.6713 | 11.9772 | 0.4396 | 30.5661 | |
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| 3.5641 | 11.0 | 814 | 0.6651 | 14.0436 | 0.4506 | 30.2 | |
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| 3.5641 | 12.0 | 888 | 0.6678 | 13.2632 | 0.4514 | 31.0542 | |
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| 3.5641 | 13.0 | 962 | 0.6677 | 14.0924 | 0.4563 | 29.278 | |
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| 0.5121 | 14.0 | 1036 | 0.6693 | 14.746 | 0.4651 | 28.4068 | |
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| 0.5121 | 15.0 | 1110 | 0.6698 | 14.9278 | 0.4677 | 28.5153 | |
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| 0.5121 | 16.0 | 1184 | 0.6700 | 14.7431 | 0.4674 | 28.9288 | |
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| 0.5121 | 17.0 | 1258 | 0.6744 | 15.2934 | 0.4701 | 28.8678 | |
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| 0.5121 | 18.0 | 1332 | 0.6741 | 15.6776 | 0.4712 | 28.3492 | |
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| 0.5121 | 19.0 | 1406 | 0.6772 | 14.942 | 0.4707 | 28.9695 | |
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| 0.5121 | 20.0 | 1480 | 0.6766 | 15.3416 | 0.4723 | 28.0271 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
