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saved_model_git-base

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+ ---
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+ license: mit
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - rouge
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+ - bleu
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+ model-index:
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+ - name: saved_model_git-base
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+ results:
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+ - task:
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+ name: Causal Language Modeling
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+ type: text-generation
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+ dataset:
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+ name: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Rouge1
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+ type: rouge
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+ value: 0.2892828477539953
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+ - name: Bleu
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+ type: bleu
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+ value: 0.09269389807461934
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # saved_model_git-base
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+
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+ This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3234
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+ - Wer Score: 2.8324
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+ - Rouge1: 0.2893
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+ - Rouge2: 0.1553
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+ - Rougel: 0.2580
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+ - Rougelsum: 0.2581
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+ - Meteor: 0.4702
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+ - Bleu: 0.0927
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+ - Bleu precisions: [0.19773408749396806, 0.11271121828401212, 0.07041869841876251, 0.047040063834047824]
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 112
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+ - eval_batch_size: 112
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 224
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 40
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer Score | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor | Bleu | Bleu precisions |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:------:|:---------:|:------:|:------:|:-------------------------------------------------------------------------------------:|
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+ | 0.7742 | 1.7 | 1000 | 0.2768 | 3.6442 | 0.2183 | 0.1135 | 0.1960 | 0.1960 | 0.4151 | 0.0767 | [0.16930678895753237, 0.09557836133795246, 0.05745933755000988, 0.03719485051124419] |
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+ | 0.2757 | 3.4 | 2000 | 0.2530 | 3.5078 | 0.2344 | 0.1280 | 0.2111 | 0.2111 | 0.4438 | 0.0866 | [0.18094996558095722, 0.1062941982267445, 0.0660909502265641, 0.04415642702225686] |
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+ | 0.2558 | 5.11 | 3000 | 0.2431 | 3.5981 | 0.2321 | 0.1292 | 0.2104 | 0.2104 | 0.4488 | 0.0871 | [0.17891643241063893, 0.10662635003059268, 0.06724109416177619, 0.04494076673073175] |
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+ | 0.2435 | 6.81 | 4000 | 0.2340 | 3.5928 | 0.2351 | 0.1337 | 0.2139 | 0.2139 | 0.4576 | 0.0909 | [0.18105927626402774, 0.11015864269439932, 0.07084035989457634, 0.04838043468996083] |
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+ | 0.2341 | 8.51 | 5000 | 0.2283 | 3.5825 | 0.2379 | 0.1374 | 0.2168 | 0.2168 | 0.4641 | 0.0935 | [0.18283567465782452, 0.11268246996142758, 0.07331907529684983, 0.0505040077040832] |
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+ | 0.2265 | 10.21 | 6000 | 0.2246 | 3.5432 | 0.2424 | 0.1415 | 0.2211 | 0.2211 | 0.4718 | 0.0961 | [0.1857985595024412, 0.11537502356732557, 0.07570186326781568, 0.05252149630642548] |
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+ | 0.2203 | 11.91 | 7000 | 0.2207 | 3.4183 | 0.2511 | 0.1480 | 0.2298 | 0.2298 | 0.4795 | 0.0973 | [0.1857490628497766, 0.11647364168304132, 0.07701231893556303, 0.053783746414351505] |
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+ | 0.2143 | 13.62 | 8000 | 0.2182 | 3.1230 | 0.2718 | 0.1608 | 0.2485 | 0.2485 | 0.4922 | 0.0985 | [0.1871246642347378, 0.11779841874899488, 0.07811713540422874, 0.054763192765544234] |
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+ | 0.2094 | 15.32 | 9000 | 0.2168 | 3.5355 | 0.2473 | 0.1472 | 0.2257 | 0.2257 | 0.4833 | 0.0998 | [0.18881918229416875, 0.1190513020004344, 0.0792775774175806, 0.05572707164637924] |
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+ | 0.2038 | 17.02 | 10000 | 0.2160 | 3.4545 | 0.2543 | 0.1509 | 0.2310 | 0.2310 | 0.4891 | 0.1007 | [0.1902420103501397, 0.11991652989168618, 0.07994424738353746, 0.05637425111890453] |
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+ | 0.1967 | 18.72 | 11000 | 0.2173 | 3.3373 | 0.2641 | 0.1559 | 0.2388 | 0.2388 | 0.4945 | 0.1016 | [0.19316197072023297, 0.12102576025295267, 0.08048056261991486, 0.056670825893034946] |
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+ | 0.1884 | 20.43 | 12000 | 0.2221 | 3.0745 | 0.2862 | 0.1681 | 0.2584 | 0.2584 | 0.5010 | 0.1039 | [0.19918408147408226, 0.12401633746143571, 0.08199896497939006, 0.05751641776005364] |
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+ | 0.179 | 22.13 | 13000 | 0.2294 | 2.9232 | 0.3017 | 0.1747 | 0.2710 | 0.2710 | 0.5036 | 0.1027 | [0.19968719829990308, 0.12253582577044991, 0.08059124511363425, 0.05632795013912335] |
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+ | 0.1676 | 23.83 | 14000 | 0.2358 | 2.8863 | 0.3017 | 0.1741 | 0.2714 | 0.2714 | 0.5022 | 0.1041 | [0.20393480253606525, 0.12448069284204198, 0.08151878403794971, 0.056659933056666174] |
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+ | 0.1562 | 25.53 | 15000 | 0.2478 | 2.8259 | 0.3036 | 0.1730 | 0.2728 | 0.2728 | 0.4980 | 0.1039 | [0.20674579460384884, 0.12461471833607483, 0.08099930020993702, 0.055934016820835084] |
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+ | 0.1463 | 27.23 | 16000 | 0.2588 | 2.7604 | 0.3066 | 0.1728 | 0.2748 | 0.2748 | 0.4965 | 0.1021 | [0.2053763571102686, 0.12266641124678603, 0.07922919125284283, 0.05440373665093972] |
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+ | 0.1367 | 28.94 | 17000 | 0.2681 | 2.8889 | 0.2945 | 0.1650 | 0.2642 | 0.2643 | 0.4872 | 0.0987 | [0.2002396944625925, 0.11880872600001989, 0.0763248805512799, 0.052217189262938515] |
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+ | 0.1276 | 30.64 | 18000 | 0.2811 | 2.7054 | 0.3062 | 0.1696 | 0.2743 | 0.2743 | 0.4891 | 0.1009 | [0.2075584522604372, 0.12181684833069355, 0.07765563632441028, 0.05272764092006992] |
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+ | 0.1198 | 32.34 | 19000 | 0.2925 | 2.8151 | 0.2945 | 0.1616 | 0.2633 | 0.2633 | 0.4811 | 0.0972 | [0.20216857878455405, 0.11754981616789531, 0.07455271799398129, 0.0503977409292061] |
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+ | 0.1133 | 34.04 | 20000 | 0.3031 | 2.8563 | 0.2910 | 0.1580 | 0.2599 | 0.2599 | 0.4758 | 0.0955 | [0.20084995564059396, 0.11580160477587004, 0.07298742511539595, 0.04909252992133042] |
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+ | 0.1066 | 35.74 | 21000 | 0.3114 | 2.8931 | 0.2864 | 0.1548 | 0.2557 | 0.2557 | 0.4715 | 0.0930 | [0.1969209496796176, 0.11291130154563171, 0.07089499015389361, 0.047528935183720734] |
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+ | 0.1018 | 37.45 | 22000 | 0.3191 | 2.8514 | 0.2884 | 0.1552 | 0.2572 | 0.2572 | 0.4708 | 0.0931 | [0.19810372685233638, 0.11312981011747879, 0.0707889148571459, 0.04735648678945608] |
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+ | 0.0982 | 39.15 | 23000 | 0.3234 | 2.8324 | 0.2893 | 0.1553 | 0.2580 | 0.2581 | 0.4702 | 0.0927 | [0.19773408749396806, 0.11271121828401212, 0.07041869841876251, 0.047040063834047824] |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.29.2
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+ - Pytorch 2.0.0
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+ - Datasets 2.12.0
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+ - Tokenizers 0.13.3