End of training

README.md

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+---
+license: apache-2.0
+library_name: peft
+tags:
+- generated_from_trainer
+base_model: google/mt5-base
+metrics:
+- bleu
+- rouge
+model-index:
+- name: mt5-base-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. -->
+
+# mt5-base-ICFOSS-malayalam_Hindi_Translator
+
+This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2179
+- Bleu: 6.2035
+- Rouge: {'rouge1': 0.2667970960136926, 'rouge2': 0.14574925525428614, 'rougeL': 0.26511828595423204, 'rougeLsum': 0.26501665904942706}
+- Chrf: {'score': 23.454551827072866, '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                                                                       |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------:|
+| 2.5515        | 1.0   | 4315  | 1.2874          | 5.8306 | {'rouge1': 0.2660910934739513, 'rouge2': 0.14404792849379128, 'rougeL': 0.26384549634107013, 'rougeLsum': 0.2637751499455684}   | {'score': 22.571342084258088, 'char_order': 6, 'word_order': 0, 'beta': 2} |
+| 1.9143        | 2.0   | 8630  | 1.2319          | 6.1128 | {'rouge1': 0.263256301663898, 'rouge2': 0.14256738224583015, 'rougeL': 0.261282034035635, 'rougeLsum': 0.2613517649673947}      | {'score': 23.235214776547263, 'char_order': 6, 'word_order': 0, 'beta': 2} |
+| 1.8644        | 3.0   | 12945 | 1.2192          | 6.2145 | {'rouge1': 0.2670714744552978, 'rouge2': 0.14606073298261613, 'rougeL': 0.2652594809906982, 'rougeLsum': 0.26489596193447795}   | {'score': 23.438449086905997, 'char_order': 6, 'word_order': 0, 'beta': 2} |
+| 1.8539        | 4.0   | 17260 | 1.2179          | 6.2043 | {'rouge1': 0.26678061058524805, 'rouge2': 0.14565482302690236, 'rougeL': 0.26489350144733725, 'rougeLsum': 0.26477198178581135} | {'score': 23.464895899326955, 'char_order': 6, 'word_order': 0, 'beta': 2} |
+| 1.8525        | 5.0   | 21575 | 1.2179          | 6.2035 | {'rouge1': 0.2667970960136926, 'rouge2': 0.14574925525428614, 'rougeL': 0.26511828595423204, 'rougeLsum': 0.26501665904942706}  | {'score': 23.454551827072866, '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

adapter_model.safetensors

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