End of training

README.md

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+---
+license: cc-by-nc-4.0
+library_name: peft
+tags:
+- generated_from_trainer
+metrics:
+- bleu
+- rouge
+base_model: facebook/nllb-200-3.3B
+model-index:
+- name: nllb-200-3.3B-Malayalam_English_Translationt_nllb6
+  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-200-3.3B-Malayalam_English_Translationt_nllb6
+
+This model is a fine-tuned version of [facebook/nllb-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0031
+- Bleu: 37.4644
+- Rouge: {'rouge1': 0.6947858221991348, 'rouge2': 0.47528501248267296, 'rougeL': 0.643592904253675, 'rougeLsum': 0.6438336053077185}
+- Chrf: {'score': 63.562323751931785, '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: 8
+- eval_batch_size: 8
+- 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                                                                       |
+|:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------:|
+| 1.1299        | 1.0   | 9400  | 1.0473          | 35.4794 | {'rouge1': 0.6827076206592405, 'rouge2': 0.4567713815837643, 'rougeL': 0.6303031579761407, 'rougeLsum': 0.6303637744842896} | {'score': 62.07772367684291, 'char_order': 6, 'word_order': 0, 'beta': 2}  |
+| 1.0391        | 2.0   | 18800 | 1.0172          | 36.5551 | {'rouge1': 0.6898802619220783, 'rouge2': 0.4678566080033477, 'rougeL': 0.6376152634193879, 'rougeLsum': 0.6378050818770977} | {'score': 62.79493404105809, 'char_order': 6, 'word_order': 0, 'beta': 2}  |
+| 0.9772        | 3.0   | 28200 | 1.0047          | 37.1999 | {'rouge1': 0.6940761673780116, 'rouge2': 0.4729467289482048, 'rougeL': 0.6422221741064402, 'rougeLsum': 0.6423854506325695} | {'score': 63.383659426629755, 'char_order': 6, 'word_order': 0, 'beta': 2} |
+| 0.9322        | 4.0   | 37600 | 1.0021          | 37.3505 | {'rouge1': 0.6946177869994575, 'rouge2': 0.47460537713160267, 'rougeL': 0.643360432984222, 'rougeLsum': 0.6434552650502989} | {'score': 63.44418689943615, 'char_order': 6, 'word_order': 0, 'beta': 2}  |
+| 0.9109        | 5.0   | 47000 | 1.0031          | 37.4644 | {'rouge1': 0.6947858221991348, 'rouge2': 0.47528501248267296, 'rougeL': 0.643592904253675, 'rougeLsum': 0.6438336053077185} | {'score': 63.562323751931785, 'char_order': 6, 'word_order': 0, 'beta': 2} |
+
+
+### Framework versions
+
+- PEFT 0.7.2.dev0
+- Transformers 4.36.1
+- Pytorch 2.0.1+cu117
+- Datasets 2.13.1
+- Tokenizers 0.15.0

runs/Dec29_05-37-45_arungpu/events.out.tfevents.1703828266.arungpu.352.0

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