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-Hindi_Malayalam_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-Hindi_Malayalam_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: 0.5883
+- Bleu: 32.3133
+- Rouge: {'rouge1': 0.4065168353303946, 'rouge2': 0.2762287202150305, 'rougeL': 0.3947284265080875, 'rougeLsum': 0.3952044100349186}
+- Chrf: {'score': 69.00192526889488, '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                                                                      |
+|:-------------:|:-----:|:-----:|:---------------:|:-------:|:----------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|
+| 0.7199        | 1.0   | 4698  | 0.6215          | 29.8169 | {'rouge1': 0.405762448982788, 'rouge2': 0.2722973168668976, 'rougeL': 0.3920717141056125, 'rougeLsum': 0.39226265285587325}  | {'score': 67.671645233609, 'char_order': 6, 'word_order': 0, 'beta': 2}   |
+| 0.6269        | 2.0   | 9396  | 0.5960          | 31.5169 | {'rouge1': 0.4074341383663418, 'rouge2': 0.2754310444010575, 'rougeL': 0.3950826699767377, 'rougeLsum': 0.3954069337543914}  | {'score': 68.50668767972792, 'char_order': 6, 'word_order': 0, 'beta': 2} |
+| 0.5962        | 3.0   | 14094 | 0.5891          | 32.4131 | {'rouge1': 0.4065168353303946, 'rouge2': 0.2774409260882534, 'rougeL': 0.3949096085748628, 'rougeLsum': 0.39546625690693493} | {'score': 68.94783702655978, 'char_order': 6, 'word_order': 0, 'beta': 2} |
+| 0.5855        | 4.0   | 18792 | 0.5882          | 32.4648 | {'rouge1': 0.4065168353303946, 'rouge2': 0.2762287202150305, 'rougeL': 0.3947284265080875, 'rougeLsum': 0.3952044100349186}  | {'score': 69.10087499970177, 'char_order': 6, 'word_order': 0, 'beta': 2} |
+| 0.5835        | 5.0   | 23490 | 0.5883          | 32.3133 | {'rouge1': 0.4065168353303946, 'rouge2': 0.2762287202150305, 'rougeL': 0.3947284265080875, 'rougeLsum': 0.3952044100349186}  | {'score': 69.00192526889488, '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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