End of training

README.md

@@ -1,29 +1,95 @@
+---
+library_name: transformers
+license: mit
+base_model: ai4bharat/indictrans2-en-indic-dist-200M
+tags:
+- generated_from_trainer
+metrics:
+- bleu
+model-index:
+- name: iitb_en_indic_robust_punctuation_model
+  results: []
+---
 
-# iitb-en-indic-only-punct
+<!-- 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. -->
 
-This repository contains a fine-tuned version of the IndicTrans2 (200M) model
-trained for **English (Latn) → Marathi (Deva)** translation *without punctuation*.
+# iitb_en_indic_robust_punctuation_model
 
-### Model details
-- Base model: IndicTrans2 (200M)
-- Task: Machine Translation
-- Language pair: en_Latn → mar_Deva
-- Special modification: punctuation removed during preprocessing
-- Finetuned at IIT Bombay.
+This model is a fine-tuned version of [ai4bharat/indictrans2-en-indic-dist-200M](https://huggingface.co/ai4bharat/indictrans2-en-indic-dist-200M) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2679
+- Bleu: 12.6319
+- Chrfpp: 35.424
+- Comet: 0.549
+- Bleurt: None
+- Gen Len: 20.8721
 
-### Training information
-Trained on custom student dataset using 🤗 Transformers.
+## Model description
 
-### Intended use
-Research, MT evaluation, academic experiments, punctuation-robust translation.
+More information needed
 
-### License
-MIT License
+## Intended uses & limitations
 
-### Tags
-- transformation
-- machine-translation
-- english
-- marathi
-- indic
-- iitb
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 8
+
+### Training results
+
+| Training Loss | Epoch  | Step   | Validation Loss | Bleu    | Chrfpp  | Comet  | Bleurt | Gen Len |
+|:-------------:|:------:|:------:|:---------------:|:-------:|:-------:|:------:|:------:|:-------:|
+| 0.4295        | 0.2530 | 6000   | 0.4148          | 9.3672  | 31.5376 | 0.531  | None   | 20.8761 |
+| 0.4049        | 0.5059 | 12000  | 0.3887          | 9.7036  | 32.0364 | 0.5341 | None   | 20.8717 |
+| 0.408         | 0.7589 | 18000  | 0.3717          | 10.0257 | 32.395  | 0.5361 | None   | 20.8778 |
+| 0.3392        | 1.0119 | 24000  | 0.3609          | 10.2988 | 32.7193 | 0.5377 | None   | 20.8761 |
+| 0.3328        | 1.2649 | 30000  | 0.3508          | 10.4288 | 32.9579 | 0.5393 | None   | 20.8767 |
+| 0.3216        | 1.5178 | 36000  | 0.3408          | 10.7134 | 33.2408 | 0.5413 | None   | 20.8727 |
+| 0.3236        | 1.7708 | 42000  | 0.3333          | 10.7915 | 33.3923 | 0.5412 | None   | 20.8762 |
+| 0.2862        | 2.0238 | 48000  | 0.3286          | 10.8948 | 33.499  | 0.5414 | None   | 20.8747 |
+| 0.293         | 2.2768 | 54000  | 0.3217          | 11.0627 | 33.6756 | 0.5426 | None   | 20.8708 |
+| 0.2839        | 2.5297 | 60000  | 0.3168          | 11.1573 | 33.8408 | 0.5432 | None   | 20.8743 |
+| 0.2758        | 2.7827 | 66000  | 0.3109          | 11.3293 | 33.9424 | 0.5433 | None   | 20.8745 |
+| 0.2667        | 3.0357 | 72000  | 0.3075          | 11.3372 | 34.0924 | 0.5441 | None   | 20.8726 |
+| 0.2618        | 3.2886 | 78000  | 0.3040          | 11.5382 | 34.275  | 0.5452 | None   | 20.871  |
+| 0.2657        | 3.5416 | 84000  | 0.2997          | 11.6458 | 34.3695 | 0.5451 | None   | 20.8744 |
+| 0.2625        | 3.7946 | 90000  | 0.2961          | 11.7085 | 34.4786 | 0.5461 | None   | 20.8709 |
+| 0.2483        | 4.0476 | 96000  | 0.2931          | 11.8339 | 34.5484 | 0.5461 | None   | 20.8726 |
+| 0.2348        | 4.3005 | 102000 | 0.2897          | 11.8966 | 34.6524 | 0.5467 | None   | 20.8713 |
+| 0.2473        | 4.5535 | 108000 | 0.2870          | 11.9908 | 34.7337 | 0.5469 | None   | 20.8717 |
+| 0.2569        | 4.8065 | 114000 | 0.2829          | 12.0335 | 34.7971 | 0.5466 | None   | 20.8748 |
+| 0.2164        | 5.0594 | 120000 | 0.2822          | 12.1516 | 34.9439 | 0.5476 | None   | 20.8711 |
+| 0.2297        | 5.3124 | 126000 | 0.2802          | 12.2298 | 35.0421 | 0.548  | None   | 20.8724 |
+| 0.2261        | 5.5654 | 132000 | 0.2784          | 12.2563 | 35.0484 | 0.5481 | None   | 20.8713 |
+| 0.224         | 5.8184 | 138000 | 0.2749          | 12.3494 | 35.1261 | 0.5478 | None   | 20.8722 |
+| 0.2108        | 6.0713 | 144000 | 0.2750          | 12.3841 | 35.1321 | 0.5478 | None   | 20.8729 |
+| 0.2259        | 6.3243 | 150000 | 0.2726          | 12.4555 | 35.2263 | 0.5485 | None   | 20.871  |
+| 0.2172        | 6.5773 | 156000 | 0.2712          | 12.5084 | 35.2688 | 0.5484 | None   | 20.8734 |
+| 0.2139        | 6.8303 | 162000 | 0.2706          | 12.5631 | 35.3595 | 0.5489 | None   | 20.8732 |
+| 0.1997        | 7.0832 | 168000 | 0.2699          | 12.5653 | 35.367  | 0.5489 | None   | 20.8736 |
+| 0.2075        | 7.3362 | 174000 | 0.2690          | 12.5846 | 35.3799 | 0.5491 | None   | 20.8711 |
+| 0.199         | 7.5892 | 180000 | 0.2686          | 12.6322 | 35.4432 | 0.5493 | None   | 20.8724 |
+| 0.204         | 7.8421 | 186000 | 0.2679          | 12.6319 | 35.424  | 0.549  | None   | 20.8721 |
+
+
+### Framework versions
+
+- Transformers 4.53.2
+- Pytorch 2.4.0a0+f70bd71a48.nv24.06
+- Datasets 2.21.0
+- Tokenizers 0.21.4

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