End of training

README.md

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+---
+license: cc-by-nc-4.0
+library_name: peft
+tags:
+- generated_from_trainer
+base_model: facebook/nllb-200-1.3B
+metrics:
+- bleu
+- rouge
+model-index:
+- name: nllb-200-1.3B-ICFOSS-malayalam_Hindi_Translator
+  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. -->
+
+# nllb-200-1.3B-ICFOSS-malayalam_Hindi_Translator
+
+This model is a fine-tuned version of [facebook/nllb-200-1.3B](https://huggingface.co/facebook/nllb-200-1.3B) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3788
+- Bleu: 62.5154
+- Rouge: {'rouge1': 0.42504662037099206, 'rouge2': 0.2891987093258279, 'rougeL': 0.4211514655126128, 'rougeLsum': 0.42156526904087943}
+- Chrf: {'score': 79.24933104383702, 'char_order': 6, 'word_order': 0, 'beta': 2}
+
+## 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: 0.0002
+- 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: cosine
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Rouge                                                                                                                          | Chrf                                                                      |
+|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|
+| 0.5095        | 1.0   | 4698  | 0.4099          | 59.5376 | {'rouge1': 0.4220305313233426, 'rouge2': 0.2866519629954242, 'rougeL': 0.41646494668344247, 'rougeLsum': 0.4167340351207185}   | {'score': 77.52631821685847, 'char_order': 6, 'word_order': 0, 'beta': 2} |
+| 0.4213        | 2.0   | 9396  | 0.3842          | 61.7541 | {'rouge1': 0.4247871478803683, 'rouge2': 0.28898946927686797, 'rougeL': 0.42099815319030365, 'rougeLsum': 0.4209781732451786}  | {'score': 78.54007352748269, 'char_order': 6, 'word_order': 0, 'beta': 2} |
+| 0.3888        | 3.0   | 14094 | 0.3785          | 62.2691 | {'rouge1': 0.42665978089706913, 'rouge2': 0.28916951694997156, 'rougeL': 0.42136280849134333, 'rougeLsum': 0.4219221144613403} | {'score': 79.11003191466068, 'char_order': 6, 'word_order': 0, 'beta': 2} |
+| 0.3764        | 4.0   | 18792 | 0.3785          | 62.4514 | {'rouge1': 0.42373682879235186, 'rouge2': 0.2891987093258279, 'rougeL': 0.41970156954196886, 'rougeLsum': 0.4201735443294585}  | {'score': 79.20088697777769, 'char_order': 6, 'word_order': 0, 'beta': 2} |
+| 0.3741        | 5.0   | 23490 | 0.3788          | 62.5154 | {'rouge1': 0.42504662037099206, 'rouge2': 0.2891987093258279, 'rougeL': 0.4211514655126128, 'rougeLsum': 0.42156526904087943}  | {'score': 79.24933104383702, 'char_order': 6, 'word_order': 0, 'beta': 2} |
+
+
+### Framework versions
+
+- PEFT 0.10.0
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.0
+- Tokenizers 0.19.1

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